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Bulletin 71(MCC Research Report 92-03)
RAINFALL FREQUENCY ATLAS OF THE MIDWEST
by Floyd A. Huff and James R. Angel
Midwestern Climate CenterClimate Analysis Center
National Weather ServiceNational Oceanic and Atmospheric Administration
Illinois State Water SurveyA Division of the Illinois Department of Energy and Natural Resources
1992
and
Bulletin 71(MCC Research Report 92-03)
RAINFALL FREQUENCY ATLAS OF THE MIDWEST
by Floyd A. Huff and James R. Angel
Title: Rainfall Frequency Atlas of the Midwest.
Abstract: This report presents the results and methodology of an intense study of rainfallfrequency relationships throughout the Midwest (Illinois, Indiana, Iowa, Kentucky,Michigan, Minnesota, Missouri, Ohio, and Wisconsin). Using primarily 275 long-termdaily reporting stations from the National Weather Service (NWS) cooperative networksupplemented by 134 daily reporting stations with shatter records, rainfall amounts havebeen determined for recurrence intervals from 2 months to 100 years and for durations of5 minutes to 10 days. The results are presented as maps and as climate division averagesin tabular form. Several special raingage networks were used to develop relationshipsbetween amounts for 24 hours and less. This report also examines the time distributions ofheavy rainfall over time, and other storm characteristics such as storm orientation andmovement. The assumption of spatially independent observations between stations is alsodiscussed.
Reference: Huff, Floyd A., and James R. Angel. Rainfall Frequency Atlas of the Midwest.Illinois State Water Survey, Champaign, Bulletin 71, 1992.
Indexing Terms: Climatology, heavy rainfall, hydroclimatology, hydrometeorology,Midwest, extreme value distributions, climate change.
The Midwestern Climate Center (MCC) is funded by a grant from the Climate Analysis Center,National Weather Service, National Oceanic and Atmospheric Administration,
U.S. Department of Commerce
STATE OF ILLINOISHON. JIM EDGAR, Governor
DEPARTMENT OF ENERGY AND NATURAL RESOURCESJohn S. Moore, B.S., Director
BOARD OF NATURAL RESOURCES AND CONSERVATlON
John S. Moore, B.S., Chair
Robert H. Benton, B.S.C.E., Engineering
Donna M. Jurdy, Ph.D., Geology
H.S. Gutowsky, Ph.D., Chemistry
Roy L. Taylor, Ph.D., Plant Biology
Robert L. Metcalf, Ph.D., Biology
W.R. “Reg” Gomes, Ph.D.University of Illinois
John H. Yopp, Ph.D.Southern Illinois University
STATE WATER SURVEY DIVISIONJOHN T. O’CONNOR, Chief
2204 GRIFFITH DRIVECHAMPAIGN, ILLINOIS 61820-7495
1992
ISSN 0360-9804
Funds derived from grants and contracts administered bythe University of Illinois were used to produce this report.
This report was printed on recycled and recyclable paper.
Printed by authority of the State of Illinois (12-92-1.6M)
CONTENTS
Page
Introduction .................................................................................................................................. 1Storms in the Midwest ........................................................................................................ 1Rationale for the Study ....................................................................................................... 1Organization of the Report .................................................................................................. 2Basic Considerations........................................................................................................... 2Pilot Study ......................................................................................................................... 3
Information Accumulated for Each State................................................................. 3Acknowledgments ............................................................................................................. 3
Part 1. Analyses ............................................................................................................................. 51. Data and Analytical Approach .................................................................................................... 5
2. Statistical Methods..................................................................................................................... 7Background........................................................................................................................ 7Analytical Method Employed in the Nine-State Study........................................................... 7Comparison of Huff-Angel, L-moments, and Maximum Likelihood Methods'Fitting Procedures for Selected States .................................................................................. 8
Maximum Likelihood Method ................................................................................. 8L-moments Method................................................................................................. 8L-moments Regions ................................................................................................ 8Results ................................................................................................................... 9
Indiana ....................................................................................................... 9Minnesota................................................................................................... 10
3. Frequency Distributions of Heavy Rainfall Events ....................................................................... 18Point Rainfall Frequency Distributions................................................................................. 18Areal Mean Frequency Distributions.................................................................................... 18
4. Time Distributions of Rainfall in Heavy Storms........................................................................... 20Method and Results of Analysis........................................................................................... 20Application of Results......................................................................................................... 20Using Results in Structural Design Problems: Case Studies ................................................... 20
Case One................................................................................................................ 20Case Two ............................................................................................................... 20Case Three ............................................................................................................. 20
5. Seasonal Distributions of Heavy Rainfall..................................................................................... 25Background........................................................................................................................ 25Analysis and Results........................................................................................................... 25
Seasonal Precipitation ............................................................................................. 25Seasonal Distribution of Heavy Rainstorms ............................................................. 25Rainfall Frequencies by Season................................................................................ 25
Summary ........................................................................................................................... 28
CONTENTS (Concluded)
6. Fluctuations in Frequency Distributions of Heavy Rainstorms in the Midwest ................................ 32Background............................................................................................................................. 32Analytical Approach and Results ............................................................................................ 32
Illinois ........................................................................................................................ 32The Midwest .............................................................................................................. 33
Summary and Conclusions ..................................................................................................... 34
7. Spatial Characteristics of Heavy Rainstorms in the Midwest ........................................................... 36Relation Between Point and Areal Mean Rainfall Frequency .................................................. 36Storm Shape ........................................................................................................................... 36Storm Orientation.................................................................................................................... 37Storm Movement .................................................................................................................... 37
8. Independence of Extreme Rainfall Events ....................................................................................... 38Examples of Outstanding Storms ............................................................................................ 38Additional Analyses ............................................................................................................... 41Summary ................................................................................................................................ 44
9. Variability within Climatic Sections................................................................................................ 45
10. General Summary and Conclusions............................................................................................... 48Data and Analytical Approach ................................................................................................ 48Statistical Methods ................................................................................................................. 48Frequency Distributions of Heavy Rainfall Events .................................................................. 48
Point Rainfall Frequency Distributions ....................................................................... 48Areal Mean Rainfall Frequency Distributions ............................................................. 48
Time Distributions of Rainfall in Heavy Storms ...................................................................... 49Seasonal Distribution of Heavy Rainfall ................................................................................. 49Temporal Fluctuations in Frequency Distribution of Heavy Rainstorms ................................... 49Other Studies........................................................................................................................... 49
References........................................................................................................................................... 50
Part 2. Spatial Distribution Maps and Sectional Mean Frequency Distribution Tables .......................... 53
FIGURES
Page
Part 1. Analyses1. Climatic sections for the Midwest ........................................................................................... 42. Stations used to derive the rainfall frequencies ......................................................................... 53. Typical sectional curves in Illinois for various recurrence intervals ............................................ 74. L-moments groups for Indiana ................................................................................................ 95. L-moments groups for Minnesota ............................................................................................ 96. Correlation between L-moments and Huff-Angel methods for 2-, 5-, 10-, 25-, 50-, and 100-year
recurrence intervals, and 24-hour rainfall amounts .................................................................... 137. Curve-fitting comparisons for 1-day amounts ........................................................................... 148. Histogram comparisons for 1-day rainfall amounts in Indiana ................................................... 149. Comparison of Huff-Angel and L-moments methods for 100-year, 24-hour rainfall in Indiana .... 15
10. Histogram comparisons for 1-day rainfall in Minnesota ........................................................... 1611. Top-ranked 10-day storms by season (1 = cold, 0 = warm) ....................................................... 2812. Stations used in comparing seasonal variations in frequency curves ........................................... 2813. Seasonal rainfall frequency curves .......................................................................................... 2914. Ratio of seasonal curve to annual curve amounts expressed in percentages 3115. Ratio for two 40-year periods (1941-1980 and 1901-1940) for 2-year, 24-hour storms ................ 3216. Stations used in temporal change study .................................................................................... 3417. Radio pattern for 2-year, 24-hour storms ................................................................................. 3418. Radio pattern for 5-year, 24-hour storms.................................................................................. 3419. Mean shape factor for heavy storms......................................................................................... 3720. Areal extent and magnitude of storm of October 5-6, 1910 ....................................................... 4221. Areal extent and magnitude of storm of March 25-26, 1913 ...................................................... 43
Part 2. Spatial Distribution Maps and Mean Frequency Distribution Tables1. Spatial distribution of 1-hour rainfall (inches) ........................................................................... 542. Spatial distribution of 2-hour rainfall (inches) .......................................................................... 603. Spatial distribution of 3-hour rainfall (inches) .......................................................................... 664. Spatial distribution of 6-hour rainfall (inches) .......................................................................... 725. Spatial distribution of 12-hour rainfall (inches) ......................................................................... 786. Spatial distribution of 24-hour rainfall (inches) ........................................................................ 847. Spatial distribution of 48-hour rainfall (inches) ........................................................................ 908. Spatial distribution of 72-hour rainfall (inches) ........................................................................ 969. Spatial distribution of 5-day rainfall (inches) ............................................................................ 102
10. Spatial distribution of 10-day rainfall (inches) ......................................................................... 108
TABLES
Page
Part 1. Analyses1. Number of Times the 24-Hour, 100-Year Value from Technical Paper 40 Is Exceeded by State........ 22. Ratio of Maximum Period to Calendar-Day Precipitation ............................................................ 63. Average Ratio of X-Hour/24-Hour Rainfall ............................................................................... 64. Relationship Between 2-Year and Shorter Interval Frequency Values for Various Rainstorm Periods 65. Ratio of Partial Duration to Annual Maximum Frequencies ......................................................... 66. Comparison of Three Methods for Estimating 24-Hour Maximum Amounts
at Selected Return Periods for Indiana....................................................................................... 107. Performance of Huff-Angel and L-moments Methods at the 24-Hour, 100-Year Recurrence Interval
by 41 Stations in Indiana ........................................................................................................ 118. Comparison of Three Methods for Estimating 24-Hour Maximum Amounts
at Selected Return Periods for Minnesota................................................................................... 159. Performance of Huff-Angel and L-moments Methods at the 24-Hour, 100-Year Recurrence Interval
for 25 Stations in Minnesota .................................................................................................... 1710. Median Time Distributions of Heavy Storm Rainfall at a Point ................................................... 2111. Median Time Distributions of Heavy Storm Rainfall on Areas of 10 to 50 Square Miles ................ 2212. Median Time Distributions of Heavy Storm Rainfall on Areas of 50 to 400 Square Miles ............... 2313. Time Distributions of Areal Mean Rainfall on 50 to 400 Square Miles in First-Quartile Storms
at Probability Levels of 10, 50, and 90 Percent .......................................................................... 2414. Seasonal Rainfall Distribution (inches) for 1961-1990 by State ................................................... 2615. Seasonal Temperature Distribution (°F) for 1961-1990 by State................................................... 2616. Seasonal Contribution of Top-Ranked 1-Day Storms ................................................................. 2717. Curve Number (CN) and Runoff (Q) Values from Three Antecedent Moisture Conditions (AMC)
for Row Crops ...................................................................................................................... 2818. Examples of Variation in Recurrence Intervals Indicated for Maximum 24-Hour Rainfall
Between Frequency Curves Derived from 1901-1940 and 1941-1980 Data in Illinois .................... 3319. Correlation Analysis Between the Three Real Maps and Two Random Maps................................. 3520. Percentage of Stations Showing Increased Precipitation Amounts at Selected Return Periods
for 24-Hour Storms Between Two 40-Year Periods (1947-1986 and 1907-1946) ........................... 3521. Relation Between Areal Mean and Point Rainfall Frequency Distributions.................................... 3622. Orientation of Heavy Rainstorms ............................................................................................. 3723. Frequency Distribution of Heavy Raincell Movements ............................................................... 3724. Distribution of Rank 1 to 10 Amounts in October 5-6, 1910, Storm in Indiana and Illinois .............. 3925. Distribution of Rank 1 to 10 Amounts in March 25-26, 1913, Storm in Indiana and Illinois............. 4026. Distribution of Rank 1 to 10 Amounts in August 14-16, 1946, Storm in Missouri and Illinois.......... 4127. 2-Day Storms Producing 5 or More and 10 or More Rank 1-10 Events ......................................... 4428. Dispersion of Point Rainfall Frequency Distributions about Sectional Mean Distributions
for Various Recurrence Intervals and Rain Durations.................................................................. 46
Part 2. Spatial Distribution Maps and Mean Frequency Distribution Tables1. Sectional Mean Frequency Distributions for Storm Periods of 5 Minutes to 10 Days
and Recurrence Intervals of 2 Months to 100 Years in Illinois....................................................... 1142. Sectional Mean Frequency Distributions for Storm Periods of 5 Minutes to 10 Days
and Recurrence Intervals of 2 Months to 100 Years in Indiana ...................................................... 1183. Sectional Mean Frequency Distributions for Storm Periods of 5 Minutes to 10 Days
and Recurrence Intervals of 2 Months to 100 Years in Iowa.......................................................... 121
TABLES (Concluded)
4. Sectional Mean Frequency Distributions for Storm Periods of 5 Minutes to 10 Daysand Recurrence Intervals of 2 Months to 100 Years in Kentucky ................................................ 124
5. Sectional Mean Frequency Distributions for Storm Periods of 5 Minutes to 10 Daysand Recurrence Intervals of 2 Months to 100 Years in Michigan ................................................ 126
6. Sectional Mean Frequency Distributions for Storm Periods of 5 Minutes to 10 Daysand Recurrence Intervals of 2 Months to 100 Years in Minnesota ............................................... 130
7. Sectional Mean Frequency Distributions for Storm Periods of 5 Minutes to 10 Daysand Recurrence Intervals of 2 Months to 100 Years in Missouri ................................................. 133
8. Sectional Mean Frequency Distributions for Storm Periods of 5 Minutes to 10 Daysand Recurrence Intervals of 2 Months to 100 Years in Ohio ....................................................... 135
9. Sectional Mean Frequency Distributions for Storm Periods of 5 Minutes to 10 Daysand Recurrence Intervals of 2 Months to 100 Years in Wisconsin ............................................... 139
INTRODUCTION
Storms in the MidwestThe type of rainstorm that most frequently produces
flash floods in the Midwest is very localized and produces alarge amount of rainfall. According to Changnon and Vogel(198l), these storms usually last from 3 to 12 hours, signifi-cantly affect fewer than 400 square miles, and have 1- to 4-hour rainfall totals in excess of 3 inches. Changnon andVogel’s study indicates that approximately 40 of these stormsoccur in an average year in Illinois, or about one storm for very1,500 square miles of territory. These storms cause seriouslocal flooding problems on farmland (crop damage) and inurban areas, and interfere with small-reservoir operations.
A larger version of the storm described above is the mostdamaging flood-producing storm experienced in the Midwestand occurs on the average of about once in two years withinthe region (Huff‚ 1986). These “blockbuster” storms gener-ally last from 12 to 24 hours, produce extremely heavy rainfallover a 2,000- to 5,000-square-mile area, and typically create10- to 12-inch amounts of rain at the storm center. Rainfallamounts in excess of the 100-year recurrence-interval valueof point rainfall commonly encompass areas of several hun-dred square miles about the storm’s center.
A substantial portion of the maximum point rainfallsrecorded in the precipitation data used in the present studyoccurred in storms of this type. Although they are rather rareoccurrences, these storms may occur in clusters. For example,two of the three blockbuster storms that occurred in Illinois in1957 took place within two weeks of each other. On the otherhand, there have been times when no blockbuster storm wasobserved for several consecutive years.
Other flood-producing storms, affecting relatively largeareas ranging from the size of a county to 20,000 or moresquare miles, result from a series of moderately intenseshowers and thunderstorms that occur intermittently for peri-ods of 1 to 10 days. Many of these individual storms wouldproduce little or no damage by themselves, but collectivelythey can cause urban drainage systems to overflow, and creeksand rivers to swell beyond capacity. This can result in bothlocalized and widespread flooding.
The frequency distributions of heavy rainfall resultingfrom the storm systems described above are of importance toengineers and others involved in designing and operatingstructures, such as storm sewers and retention ponds, that canbe affected by these events. To meet this need, our nine-statestudy has concentrated on determining rainfall frequencyrelations over a wide range of storm periods or partial stormperiods (5 minutes to 10 days) and recurrence intervals (2months to 100 years). The large-scale analysis programrequired was considered necessary to meet the diverse needsfor rainfall frequency information, both now and in theforeseeable future.
Rationale for the StudySome specific needs led to the undertaking of this study.
First, frequency relations for the Midwest had not beenupdated since Hershfield’s U.S. Weather Bureau TechnicalPaper 40 (TP40) in 1961. Second, further stimulation for thestudy resulted from recent findings (Huff and Changnon,1987) that an apparent climatic trend operated on the fre-quency distributions of heavy rainstorms in Illinois from1901-1980, which was confirmed by Huff and Angel (1990)for portions of the Midwest. Third, there was a need for moredetailed spatial description of the variations in rainfall amountsfor any given duration and recurrence interval than wasprovided in the TP40 study.
One of the problems with TP40 is that its 100-year, 24-hour values have been exceeded too frequently in certainregions of the Midwest. Table 1 summarizes the number oftimes that these values were exceeded for selected, long-termstations in each state. Assuming a binomial distribution, theprobability of exceeding a 100-year event in a given year canbe calculated for a particular station. For example, in Illinoisthe probability of exceeding a 100-year event is 0.583 with anaverage record length of 87 years. With 61 stations, one wouldexpect a 100-year event to have been exceeded approximately36 times during this period (column d in table 1) rather thanthe 69 times that were observed (column c in table 1). Theresults in Michigan are even more striking, with over threetimes the expected number of storms exceeding the 100-yearvalue. But in Missouri the TP40 values were not exceedednearly as often as expected, which suggests that these valuesare too high. For the entire Midwest, 246 storms exceeded the100-year value against an expected number of 171 storms (aratio of 1.43).
The present study has used a much larger, longer sampleof precipitation data than was available for previous U.S.studies by Yarnell (1935), Hershfield (1961), and Miller et al.(1973), and an Illinois study by Huff and Neill (1959a). Thepresent study has employed a comprehensive data samplefrom 409 stations in nine states across the Midwest (Illinois,Indiana, Iowa, Kentucky, Michigan, Minnesota, Missouri,Ohio, and Wisconsin). Records from 275 of these stations dateback to the early 1900s. Thus we were able to provide greaterspatial detail than was possible in the previous studies.Furthermore, the longer time sample should provide moreaccurate estimates of the various frequency distributions,particularly for relatively long recurrence intervals (25 yearsor more).
All the results in this report are expressed in the Englishsystem of units. It is anticipated that hydrologists and otherswho use the information will continue to use the Englishsystem in the foreseeable future. The following conversiontable can be used in converting English units to metric units.
1
(a)
IllinoisIndianaIowaKentuckyMichigan*MinnesotaMissouriOhioWisconsinMidwest
Table 1. Number of Times the 24-Hour, 100-Year Valuefrom Technical Paper 40 Is Exceeded by State
Number ofstations
614143254625444113
(b)Averagelength ofrecord
876480676067626078
(c)Number of
timesexceeded
6917201171144
2713
246
(d)Number of
timesexpected
36202412211220197
171
*From Sorrell and Hamilton, 1990
Conversion Table
MultiplyInch (in.)Mile (mi)Square mile (mi²)
By25.4
1.62.6
To obtainMillimeter (mm)Kilometer (km)Square kilometer (km²)
Organization of the ReportThis report is divided into two main parts: Analyses, and
Distribution Maps and Tables. Readers interested solely inobtaining rainfall amounts for particular durations and recur-rence intervals should see chapter 3 and part 2. Chapter 10provides a complete overview. Those interested in how thevalues were obtained should see the Introduction and chapters1 and 2, which describe why the study was undertaken, thedata sets used, and the statistical analyses that were applied.
Chapters 4, 5, and 7 provide auxiliary information aboutheavy storms in the Midwest, which may be useful for designand planning purposes. These chapters describe rainfall dis-tribution within a storm, spatial characteristics of storms, andchanges in the rainfall distribution through the seasons.
Chapter 6 addresses the issue of climate change andextreme rainfall, and documents significant changes withtime over parts of the Midwest. Chapters 6 and 8 address twoof the basic statistical assumptions of heavy rainfall events: astationary time series and spatially independent rain events.Chapter 9 discusses the dispersion of point values around theclimate section mean values found in the tables in part 2.
Basic ConsiderationsThe basic philosophy applied in the nine-state study was
that a combination of appropriate statistical techniques,
2
Ratio (c)/(d)1.920.850.830.923.381.170.201.421.861.43
guided by available meteorological and climatological knowl-edge of atmospheric processes, provides the best approach tothe problem. In so doing, it is important to remember thatthe natural laws operating in the atmosphere are not controlledby any particular statistical distribution. Within the limits ofthe data sampled (for example, 25, 50, or 100 years), however,the application of appropriate statistical analysis provides ameans of optimizing the information contained in that data.
The specific type(s) of statistical distribution that willprovide the optimal rainfall frequency relations for a givenlocation will vary depending on such factors as climate, landfeatures (topography, large water bodies, etc.), and season ofthe year (if a seasonal analysis is being performed). Thusclimatology would suggest it is doubtful whether the samestatistical distribution that provides a good fit for Chicago datawould also achieve the same degree of reliability if applied todata for Miami, Phoenix, or Seattle, where the precipitationclimates have substantially different characteristics than atChicago. For example, see Changnon’s definition of thenation’s rainfall climate zones based on analysis of hourlyrainfall amounts and their distributions (Changnon andChangnon, 1989).
It is also important to remember that any specificstatistical distribution serves only as a means of optimizinginformation contained in the data sample. One must be verycautious in extrapolating the derived frequency relationsbeyond the limits of the data. Thus if rainfall frequencyrelations have been derived from an 80-year data sample, it isreasonable to assume that the relations should be satisfactoryfor estimating the expected 100-year event, but certainly notthe 500-year event. This is too far beyond the limits of the data.In fact, there is no assurance that the natural laws affecting therainfall will continue to closely follow any particularstatistical distribution for the next 500 years. If significant
climate changes are occurring, as indicated by numerousinvestigators, then rainfall processes cannot be assumed toremain stationary in the future.
Before describing the specific procedures used in ournine-state study, it is necessary to mention another basicproblem always encountered in rainfall frequency studies.There are two sources of potential variability contained in thedata sample for a given location: natural and human-inducedvariability. The natural variability factor can cause significantdifferences to appear in the frequency distributions of twostations located within an area of apparent precipitationclimate homogeneity. This variability can be caused by oneor several storms of abnormal intensity occurring at one sta-tion and not the other, even over a long period. This is not anuncommon occurrence in regions such as the Midwest wherethunderstorms are the primary producers of heavy rainstorms.
Unfortunately, this natural variability is very difficult,if not impossible, to separate from human-induced variability,which also often affects the data sample at a particular loca-tion. This variability is influenced by such factors as improperraingage exposure, the worst source of measurement error;recording errors; and mistakes in processing rainfall data.Vogel (1988) provides some good examples of problemscreated by improper raingage exposure, data processing inad-equacies, and inadequate gage maintenance.
If isohyetal maps of rainfall frequency relations are tobe the end product of a study, some scientific judgment mustbe used in assessing such data differences between stations.These variability errors cannot be completely eliminated bystatistical treatment of the data. If areal mean frequencyrelations are derived for areas of similar precipitation climate,however, this problem can be reduced substantially.
Another important issue is the decision not to use hourlyprecipitation data to directly calculate rainfall frequencyvalues. The hourly data were not used for three reasons: theperiod of record is typically shorter than for the daily reportingstations (35 years or less in most cases); there are fewer hourlystations in the region by a factor of 2; and, most importantly,the quality of the data is much poorer than that of the dailydata. Sorrell and Hamilton (1990) came to the same conclu-sion about the drawbacks of the hourly data in their rainfallfrequency analysis of Michigan. Developing an analysisbased directly on the hourly data with the same accuracy anddetail as the daily data would have been impossible. There-fore, the hourly data were only used to develop relationshipsbetween the daily data and durations less than daily (seechapter 1 for more discussion on the technique used).
Pilot StudyInitially, a very detailed study of Illinois rainfall
frequency relations was made (Huff and Angel, 1989). Inthis study, the authors explored the use of those statisticaldistributions considered to have potential for application inIllinois based on (1) the observed characteristics of thedata sample and (2) consideration of the precipitation climateand influences generated by certain topographical features
and two large, urban areas (Chicago and St. Louis). An 83-year sample of data (1901-1983) for 61 cooperative stationsand 34 recording gage stations in and near Illinois wasavailable at the start of the pilot study.
It was assumed that the analytical techniques derivedin the Illinois study were applicable to the other eight statesin the Midwest, since there are no major changes in thegeneral precipitation climate within this region. That is,there are no changes to a tropical, desert, or maritime climatewithin the region—the general climate type is humid con-tinental. The above method of deriving analytical techniques
from a detailed investigation of one climatically represen-tative state (or area) in the region of interest is considered bythe authors to be appropriate, time-saving, and cost-effective.
Information Accumulated for Each StateFor each precipitation station in the pilot study, the
frequency distribution of rainfall amounts was determined forstorm durations of 5 minutes to 10 days and for recurrenceintervals ranging from 2 months to 100 years to adequatelymeet the needs of users. Mean rainfall frequency relationswere then calculated for each climatic section in the ninestates. The climatic trend at each station was measuredthrough use of the ratio of rainfall amounts in a 40 year-period(1947-1986) to those for the previous 40-year period (1907-1946) for selected recurrence intervals and rain durations.
From the point (station) data, frequency relations weredeveloped in the form of isohyetal maps for selected rain per-iods and recurrence intervals (those most commonly used byhydrological engineers and others). Regional maps werederived for rain periods of 1, 2, 3, 6, 12, 24, 48‚ 72, 120, and240 hours, and for recurrence intervals of 2, 5, 10, 25, 50, and100 years. Methods have been provided for computing rain-fall for the lesser used storm periods of 5 to 30 minutes, andfor recurrence intervals of 2 to 12 months.
As indicated above, areal mean relations were alsodetermined for each climatic section in each state. Section lo-cations are shown in figure 1. Results, presented in tabularform, include the entire range of rain periods and recurrenceintervals used in the point rainfall computations. Assumingapproximate homogeneity of heavy rainfall climate within asection, the average relations are considered more reliablethan point values. The mean section relationship helps mini-mize the effects of the natural variability and human-inducedsampling errors, which sometimes distort the true distributionpattern of heavy rainfall at specific sampling points (stations).
Acknowledgments
This report is the culmination of rainfall frequencyresearch originally begun in 1984 under the direction ofStanley A. Changnon. Jr., then Chief of the Illinois StateWater Survey. Although the investigation was initiallyrestricted to Illinois, it was expanded in 1988 to include thenine-state Midwest region of the Midwestern Climate Center
3
(MCC) with Stanley Changnon and Peter J. Lamb as the co-principal investigators. The work was continued and com-pleted under the general direction of Kenneth Kunkel‚ presentMCC Director.
Special appreciation goes to Stan Changnon for hisforesight, guidance, and encouragement in establishing andaccomplishing the program objectives. He and Ken Kunkelreviewed the report and made useful comments and sugges-tions. Special thanks go to Richard Katz, National Center forAtmospheric Research; Tibor Farago, Hungarian Meteoro-logical Service; and J.R.M. Hosking, IBM Research Division,for providing software for some of the extreme rainfall
analyses. Fred Nurnberger, Michigan State Climatologist,provided valuable long-term precipitation data for his state aswell as comments on the manuscript. We also thank thefollowing state climatologists for their review and commentson this project: Wayne Wendland, Illinois; Ken Scheeringa,Indiana; Harry Hillaker, Iowa; Glen Conner, Kentucky; JimZandlo, Minnesota; Wayne Decker, Missouri; Jeff Rogers,Ohio; and Pam Naber-Knox, Wisconsin.
John Brother and Linda Hascall supervised the ex-tensive drafting work required for the report. Jean Dennisontyped and assembled the report, which Eva Kingston editedand formatted.
Figure 1. Climatic sections for the Midwest
4
PART 1. ANALYSES1. DATA AND ANALYTICAL APPROACH
This study relied primarily on data from 275 dailyreporting stations of the National Weather Service (NWS)cooperative network, which had records exceeding 50 years.These data were provided in digital form by the NationalClimatic Data Center and, in some cases, keypunched by theMidwestern Climate Center from written records. The cover-age ranged from good in Illinois, Indiana, Iowa, Michigan,and Missouri to sparse in Minnesota, Wisconsin, Ohio, andKentucky. These data were supplemented by daily data from134 cooperative stations with shorter records (1948 to present),by first-order station data, and recording raingage data whereavailable (1948 to present) (figure 2).
Because the cooperative network provides only dailyamounts of rainfall, an empirical factor of 1.13 was used toconvert calendar-day rainfall to maximum 24-hour rainfall.This empirical factor was developed by NWS analysts (U.S.Weather Bureau, 1953) and confirmed by Hershfield (1961)and Huff and Neill (1959a). This factor was investigated fur-ther in the nine-state study by using all recording raingage datafor the period 1948-1987 in Indiana and Illinois. Analysis ver-ified the earlier findings that 1.13 represented the averageratio of maximum 24-hour to calendar-day rainfall in heavyrainstorms. Conversion factors of 1.05 and 1.02, respectively,were obtained for converting 2-day rainfall to maximum 48-hour rainfall and 3-day rainfall to maximum 72-hour rainfallin heavy storm events. The ratios decreased to 1.01 for 5-dayand 10-day storms. These are average factors that may varyconsiderably between storms, but should result in only smallerrors when applied to a large sample of storms, such as usedin this study. Table 2 shows the various conversion factors.
Recurrence-interval amounts for rain periods of lessthan 24 hours were obtained from average ratios of x-hour/24-hour rainfall. These ratios were determined primarily fromrecording raingage data for 1948-1983 at 34 Illinois stationsand 21 stations in adjoining states (Huff and Angel, 1989).Results of a similar study, based on the Chicago urbannetwork data for 1948-1974 (Huff and Vogel, 1976) and ratiosdeveloped by Hershfield (1961) were also considered whendetermining the empirical factors. All the information sourcesprovided ratios that were in close agreement. Results areshown in table 3.
Frequency relations are usually developed for recur-rence intervals of one year or longer. To meet some userneeds, however, it was necessary to develop frequency rela-tions for time periods shorter than 12 months. The dataanalysis showed that 2-month to 9-month frequency valuesare strongly related to the 2-year values. The x-month/24-month ratios were found to be spatially consistent for allrecurrence intervals. These ratios are shown in table 4 for
Figure 2. Stations used to derive the rainfallfrequencies
storm periods of 24 hours to 10 days. The 24-hour values arealso applicable to storm periods of less than 24-hour duration.
For each station, the data were used to determine theannual maxima time series from the highest precipitationamount recorded in each year for a given storm duration.Station (point rainfall) frequency curves were then calculatedfor the various storm rainfall durations of interest. For thisreport, however, the annual maxima values were converted topartial duration values by using the transformation factorsshown in table 5 (Huff and Neill, 1959a). The partial durationseries includes all of the high values recorded during asampling period without regard to their annual sequence.Thus all of the 50 highest values occurring in a 50-year periodwill be included in the partial duration series, but not neces-sarily in the annual maxima series. Although the annualmaxima series is more adaptable to statistical testing, ourexperience indicates that the partial duration values arepreferred by most users of heavy rainfall frequency relations,especially engineers involved in the design and operation ofwater control structures. The rainfall values are interchange-able through use of table 5.
5
6
Table 2. Ratio of Maximum Periodto Calendar-Day Precipitation
Table 3. Average Ratio of X-Hour/24-Hour Rainfall
Rain period (hours) Ratio (x-hour/24-hour)
Storm period (days) Ratio 18 0.94
1 1.13 12 0.87
2 1.05 6 0.75
3 1.02 3 0.64
5 1.01 2 0.58
10 1.01 1 0.47
0.50 (30 min.) 0.37
0.25 (15 min.) 0.27
0.17 (10 min.) 0.21
0.08 (5 min.) 0.12
Table 4. Relationship Between 2-Year and Shorter Interval Frequency Valuesfor Various Rainstorm Periods
Mean ratio (x-month to 24-month rainfall) for given rainstorm period
Stormperiod(hours)
2-month
3-month
4-month
6-month
9-month
12-month
24 0.46 0.53 0.58 0.67 0.76 0.83
48 0.44 0.51 0.57 0.66 0.76 0.83
72 0.43 0.51 0.57 0.66 0.76 0.83
120 0.42 0.50 0.57 0.66 0.76 0.83
240 0.41 0.49 0.57 0.66 0.76 0.83
Table 5. Ratio of Partial Duration to Annual Maximum Frequencies
Ratio for given recurrence interval
Precipitation period (hours) 2-year 5-year 10-year
24 1.13 1.05 1.01
48 1.09 1.02 1.01
120 1.08 1.01 1.00
240 1.08 1.01 1.00
2. STATISTICAL METHODS
BackgroundIn previous Illinois studies (Huff and Neill, 1959a;
Huff and Angel, 1989), various statistical distributions weretested for their applicability in fitting extreme rainfall datain the Midwest. These distributions included log normal,Gumbel (1941). Frechet (Gumbel, 1956), Chow (1954),Jenkinson (1955), and log-Pearson (Reich, 1972). Log-log and semi-log fitting procedures were also investigated.Recently, as part of our nine-state study, an investigationwas also made of the application of L-moments and maxi-mum likelihood fitting methods to the generalized extremevalue theory (Wallis, 1989; Hosking, 1990). Results werecompared with those generated by the Huff-Angel methoddescribed below.
No single statistical distribution was found in the earlierIllinois studies (Huff and Neill, 1959b; Huff and Angel, 1989)that would consistently provide a satisfactory fit over the widerange of rain periods and recurrence intervals required to meetuser needs. These studies generally showed that the Frechet,log-Pearson, and log-log methods provided the best fit forrecurrence intervals exceeding 2 years. These methods, how-ever, produced unsatisfactory estimates of rainfall values forrecurrence intervals of 2 months to 2 years. For these shorterintervals, log-normal and semi-log fittings of the data oftenclosely approximated the values indicated by plotting theranked observational data.
These findings support those of Sevruk and Geiger(1980) who made an extensive appraisal of distributiontypes for extremes of precipitation for the World Meteorologi-cal Organization (WMO). But Sevruk and Geiger’s worldwideappraisal did not reach a conclusion concerning thesuperiority of any particular distribution. They point out that“some distributions, however, may be superior to othersunder given seasonal and/or geographical conditions.” Thisagrees with earlier Illinois findings, which indicated thatthe Frechet distribution was most applicable to annual,spring, summer, and fall data, but the log-normal distri-bution provided the best fit for winter data (Huff andNeill, 1959b).
Analytical Method Employedin the Nine-State Study
For our nine-state study, a log-log graphical analysis,hereafter referred to as the Huff-Angel method, was used forfinal derivation of the frequency relations. This methodresulted in smooth curves, such as those illustrated in theIllinois example in figure 3. This figure shows the frequencydistribution of 24-hour maximum rainfall amounts for recur-rence intervals varying from 2 months to 100 years. A majorchange is reflected in the distribution characteristics for thetwo sectional curves near the 2-year recurrence interval.
Figure 3. Typical sectional curves in Illinoisfor various recurrence intervals
Similar curves were obtained for the various sections andindividual stations (sampling points) used in our nine-state study. The curve shape varied somewhat among sta-tions, however. For example, at some stations, the change incurvature began closer to the 5-year than the 2-year recurrenceinterval. Changes in curve characteristics also occurred some-times with increasing length of rain periods, but a smoothshape was preserved. For most stations, however, a linearfit was provided for return periods of 2 years or more.This method is more subjective than using specific statisticalmethods, such as L-moments or maximum likelihood, to fita specific statistical distribution (such as log-normal,Gumbel, etc.). However, it does allow the analyst toincorporate meteorological-climatological knowledge andother pertinent findings from the various analysis proceduresemployed in the study. For example, human-made samplingerrors were sometimes obvious in our nine-state study fromcomparison of station rainfall values within areas ofapproximately homogeneous precipitation climate. Theintegration of all available information is especially helpfulin evaluating the rarer events (outliers) appearing in somestation records.
The Huff-Angel method places acutoff on extrapolationat or near the 100-year frequency, since the data are not fittedto a specific mathematical distribution. For reasons citedearlier, however, extrapolation of any frequency relation muchbeyond the limits of the data sample (80+ years at most long-term stations) is not recommended. Furthermore, climatic andphysiographic variations can cause the “best-fit” statisticaldistribution to vary within a single state as shown by Huff andNeill (1959a).
7
Comparison of Huff-Angel, L-moments,and Maximum Likelihood Methods’ FittingProcedures for Selected States
To evaluate the maximum likelihood and L-momentsmethods, the generalized extreme value (GEV) distributionwas used because of (1) its versatility (the Gumbel and Frechetdistributions, for example, are really special cases of theGEV) and (2) the need for a uniform distribution for compari-son purposes. A literature search also indicated that the GEVdistribution would be the most appropriate statistical distribu-tion for computing point rainfall frequency relations.
Maximum Likelihood MethodThe maximum likelihood method is a standard statisti-
cal procedure used in fitting a variety of hydrological data(e.g., Kite, 1977; Farago and Katz, 1990). For the stations usedin this study, the sample size was always greater than 36 (64on average). This method should thus yield relatively unbi-ased estimates of the parameters.
L-moments MethodRecently, another method for fitting distributions
appeared in the literature, the L-moments method (Hosking,1990). This method, analogous to the method of moments(L-mean, L-skewness, etc.), uses linear combinations of orderstatistics to develop estimates of the distribution. Theoreti-cally, the advantages of this approach over the traditionalmethod of moments are the smaller impact of outliers and themore accurate inferences derived from smaller samples. Thismethod is being used by NWS in updating rainfall frequencyrelationships in the western United States (Vogel, personalcommunication, 1991).
In practice, the L-moments method is more involvedthan either the Huff-Angel or maximum likelihood methods,since it uses regional values to estimate some of the param-eters. Thus care must be taken in grouping the stations intoappropriate regions by plotting the L-skewness versus L-kurtosis to look for groupings, calculating a discordancymeasure by station to indicate potential problems, and exam-ining heterogeneity through Monte Carlo simulations. All thiscan easily be done using available software (Hosking. 1991).Once the stations are properly grouped, the precipitationamounts for various return periods can be calculated with theappropriate distribution, based on a goodness-of-fit measure.
L-moments RegionsThe L-moments technique is relatively new and thus
requires a more detailed discussion regarding its application.Because this method uses a regional approach to estimate thefrequency distributionsat individual sites, its potential advan-tages are that it minimizes the sampling errors at individualsites and maximizes the number of available observations.Two crucial factors in this approach include the ability toidentify homogeneous regions and the assumption that theindividual sites are independent of each other. Hosking and
8
Wallis (1991) describe the four steps to developing a regionalfrequency analysis.
1. Screening the data. The data are controlled to pro-vide a valid analysis. Hosking and Wallis (1991) employ adiscordancy measure based on the sample L-moments and thesample covariance matrix to identify stations that did not fitinto the group due to data errors or to identify stations thatbelong in some other group.
2. Identifying homogeneous regions. Stations aregrouped according to their statistical and geographical char-acteristics. The suggested method compares the L-covariancefrom the observed data with simulated data from a homoge-neous region (using Monte Carlo techniques). The differencesare divided by the standard deviation of the simulations tobecome the measure of heterogeneity (H). If H is less than 1,then the region is fairly homogeneous. Values greater than 2are considered fairly heterogeneous.
3. Selecting the frequency distribution. Hosking andWallis (1991) proposed a goodness-of-fit test to identifyappropriate distributions from a family of distributions. Thetest statistics (Z) are the difference between the observed andfitted regional L-kurtosis divided by the standard deviation ofthe observed L-kurtosis. Values sufficiently close to zeroindicate a “‘good” fit.
4. Calculating the regional frequency distribution. Thehomogeneous regions are used to calculate the frequencydistributions for the stations in each region.
The application of this methodology to the stations inIndiana and Minnesota proved somewhat difficult and re-quired a number of subjective decisions. Initially, the stationswere grouped by NWS climate division since these divisionsare widely accepted as areas of reasonably homogeneous cli-mate. Interestingly, several sites yielded high discordancyand heterogeneous indices indicating that they belonged inother regions. After several iterations, seven regions in Indi-ana (figure 4) and four regions in Minnesota (figure 5) wereselected. As the maps show, the final regions are not alwaysgeographically coherent. Probably the worst case is the threestations in group 1 in Minnesota (figure 5): on Lake Superior,along the Minnesota-Wisconsin border, and in the southwestcomer of the state. As Hosking and Wallis (1991) rightly pointout, however, the physical evidence should take precedenceover the statistical evidence. Therefore these three stationsshould be incorporated into the other groups. Since theL-moments method was not used for this report, however, amore sophisticated treatment of the regionalization wasnot developed.
The task of regional frequency analysis is further com-plicated by extreme rainfall events that may not be spatiallyindependent (see Chapter 8). Spatial correlations betweenstations will cause problems with the test statistics, especiallythe heterogeneity and goodness-of-fit test. Hosking and Wallis(1991) therefore recommend that these statistics only be usedas guidelines and not for hypothesis testing.
In general, assuming readily identifiable homogeneousprecipitation regions with highly independent stations, one
can take full advantage of regional analysis to overcome sam-pling errors and short records. In the application here, how-ever, the appropriateness of aregional analysis is not as clear-cut since identifying homogeneous regions is difficult andsome spatial correlation exists among extreme rainfall events.
The standard method of moments technique was notused in the comparisons due to its relatively poor performancecompared with the other techniques (based on preliminarydata). This method has been generally applied to the Gum-bel distribution.
ResultsFor comparison of the three methods, Indiana and
Minnesota were selected for their relatively diverse climaticfeatures in the Midwest region. The Huff-Angel values hadbeen previously calculated and were not influenced by theresults of the other two methods. The Huff-Angel and maxi-mum likelihood methods were applied to individual stations.On the other hand, the L-moments method was applied tohomogeneous groups of stations. The results are presented by state.
Indiana. Tables 6 and 7 summarize the differencesfound for 41 stations in Indiana from comparison of 24-hour,100-year rainfall estimates. In general, the Huff-Angel methodyielded slightly higher rainfall amounts than either the L-moments or maximum likelihood methods. The root meansquare errors (RMSE) are about the same for all three meth-ods. Analysis of the correlation between the L-moments andHuff-Angel methods shows good agreement throughout the25-year recurrence interval (table 6 and figure 6). Thisrelationship deteriorates somewhat at the longer intervals, as
Figure 4. L-moments groups for Indiana Figure 5. L-moments groups for Minnesota
expected, because the methods extrapolate beyond the data,thus increasing the uncertainty in the values. No strongevidence of a bias is present until the 100-year amounts, whichare being estimated with less than 100 years of data (35 to85-year records), are reached, and any differences in themethods become more noticeable at the rarer recurrenceintervals. Figure 7 shows examples of good (Albion, IN) andpoor agreement (Bloomington, IN).
The 100-year values from the Huff-Angel and the L-moments methods were used in a worst-case comparison.Differences will usually be largest at this return period. TheHuff-Angel method resulted in larger 100-year values at 21stations (51 percent), compared with 19 stations (46 percent)with the L-moments method. One station (2 percent) hadequal values with the two methods. The mean of the 100-yearvalues was 6.4 inches for the L-moments method and 6.6inches with the Huff-Angel method. The median difference(0.2 inch) is equivalent to a 3 percent difference. The mediandifference (0.3 inches) is equivalent to a 5 percent difference.These relatively small differences are insignificant from ameteorological standpoint. Differences much greater thanthose obtained from the two fitting methods could result fromnatural variability, human-induced variability, and extrapola-tion of the curves beyond the data to determine the 100-yearvalues. For example, for the 100-year values, the spatialvariance between the 41 stations was 1.04 inches while thevariance of the differences between the Huff-Angel and L-moments methods was 0.54 inch.
Although the data do not strictly satisfy all the assump-tions, a simple Analysis of Variance (ANOVA) model showsthat there are no significant differences in the state-wide mean
9
Returnperiod
2
5
10
25
50
100
Table 6. Comparison of Three Methodsfor Estimating 24-hour Maximum Amounts at Selected Return Periods for Indiana
Huff-Angel vs.Maximum likelihood
Huff-Angel vs.L-moments
Maximum likelihoodvs. L-moments
Mean Corre- Mean Corre- Mean Corre-difference lation difference lation difference lation(inches) (inches) (inches)
-0.03 0.98 -0.03 0.91 0.00 0.97
0.05 0.94 0.02 0.90 -0.03 0.98
0.07 0.96 0.03 0.92 -0.04 0.96
0.07 0.90 0.03 0.88 -0.04 0.89
0.06 0.81 0.04 0.79 -0.03 0.84
0.07 0.72 0.07 0.70 0.01 0.79
for the three methods. An examination of the data shows thatsome degree of skewness is present (figure 8). The estimatesfrom the maximum likelihood method are least conservative(have a longer tail), and the L-moments estimates are mostconservative with many more values lying in the middle of thedistribution. The estimates by the Huff-Angel method rankbetween the other two methods.
To summarize, there are no meteorological or statisticaldifferences in the methods used. By design, however, the L-moments method gives slightly more conservative valuesthan the other two methods. Since wearedealing with samplesfrom an unknown population, it is difficult to ascertain if moreconservative values are better or not. The more conservativeestimates may provide a relatively poor fit to the observa-tional data in some cases. For example, in figure 7, the Huff-Angel curve appears to fit the observational data better thanthe L-moments curve.
The results of the L-moments study for Indiana weremapped, analyzed, and compared with the results of the Huff-Angel method for the 100-year, 24-hour values (figure 9).Although the patterns for both methods are generally similar,some of the spatial detail is lost in the L-moments pattern(figure 9b), especially in southern Indiana. Both maps showa ridge of relatively heavy rainfall extending south-southwestfrom north-central Indiana to its southwestern border. The L-moments map (figure 9b) indicates an increase in the rainfallgradient northward along theridge—that is, the highest values(8 inches) are indicated in north-central Indians—but therainfall gradient increases from north to south on the Huff-Angel map (figure 9a). Interstate analyses showed that theridge continues south-southwest from southwestern Indianato a maximum in southeastern Illinois and western Kentucky:this agrees with the general climatic gradient of rainfall inthese midwestern states. The L-moments high in north-centralIndiana (figure 9b) was apparently produced by data from two
1 0
short-term stations at Logansport and Warsaw. As shown onthe Huff-Angel map (figure 9a), the north-central high issqueezed between lows to the west, east, and north, and is thenorthern extremity of the rainfall high.
In southern Indiana, the Huff-Angel pattern also indi-cates a low extending northeast from the southern border. Thislow appears to be an extension of relatively low 100-yearrainfall amounts over eastern Indiana, western Ohio, andeastern Kentucky (as shown by interstate analyses). Thusthere is relatively strong climatological support for thispattern anomaly. The Indiana low has been essentially elimi-nated by the L-moments fitting process.
A third region of some disagreement exists in extremenorthwestern Indiana. Here, the Huff-Angel map indicates amore intense rainfall center (9 inches) than the L-momentspattern (8 inches). This high has strong climatological supportwith respect to location and intensity from Valparaiso andLaPorte in Indiana and from stations to the west and northwestin northeastern Illinois (Kankakee, Joliet, and Aurora). The L-moments process recognizes the pattem, but appears to reducethe magnitude more than is supported by the observationaldata responsible for establishment of the pattern anomaly.
The foregoing examples are presented to emphasize thenecessity for integrating meteorological-climatological in-formation and knowledge into rainfall frequency analyses,rather than placing complete dependency on a favored statis-tical distribution. The strictly statistical approach eliminatesthe subjectivity factor, but, in so doing, it ignores importantscientific information pertinent to the problem. For the Huff-Angel and L-moments methods, the maps of the 100-yearrecurrence values showed the largest differences. All of theshorter recurrence-interval patterns, however, were in closeagreement for the two methods.
Minnesota. In Minnesota, 25 long-term stations wereused. Table 8 shows that the Huff-Angel method is in closer
11
Table 7. Performance of Huff-Angel and L-moments Methodsat the 24-Hour, 100-Year Recurrence Interval by 41 Stations in Indiana
(a) (b)Site Huff-Angel L-moments Difference Ratio (a):(b)
Albion 5.3 5.0 0.3 1.1
Anderson 5.6 5.2 0.4 1.1
Angola 6.0 5.4 0.6 1.1
Berne 4.8 5.3 -0.5 0.9
Bloomington 7.9 6.4 1.5 1.2
Bowling Green 7.5 7.2 0.3 1.0
Collegeville 5.6 6.0 -0.4 0.9
Columbus 8.6 7.1 1.5 1.2
Evansville 5.8 6.4 -0.6 0.9
Farmland 5.4 5.5 -0.1 1.0
Ft. Wayne 5.5 4.9 0.6 1.1
Frankfort 5.9 6.0 -0.1 1.0
Goshen College 6.8 5.4 1.4 1.3
Indianapolis 5.4 5.8 -0.4 0.9
Jasper 6.7 7.5 -0.8 0.9
Kokomo 7.9 6.3 1.6 1.3
Logansport 7.1 8.4 -1.3 0.8
Marion 6.0 6.5 -0.5 0.9
Markland Dam 6.2 6.3 -0.1 1.0
Moors Hill 5.7 6.3 -0.6 0.9
Mt. Vernon 6.8 7.9 -1.1 0.9
Oolitic 5.4 6.4 -1.0 0.8
Paoli 6.0 6.3 -0.3 1.0
Plymouth 5.6 5.8 -0.2 1.0
Princeton 9.7 7.9 1.8 1.2
Richmond 6.6 5.5 1.1 1.2
Rockville 7.6 6.9 0.7 1.1
Rushville 5.5 5.7 -0.2 1.0
Scottsburg 7.0 6.4 0.6 1.1
Seymour 6.6 6.1 0.5 1.1
12
Table 7. Concluded
(a) (b)
Site Huff-Angel L-moments Difference Ratio (a):(b)
South Bend 5.3 5.4 -0.1 1.0
Tell City 7.0 7.8 -0.8 0.9
Terre Haute 6.7 6.3 0.4 1.1
Valparaiso 9.0 7.9 1.1 1.1
Wabash 6.1 5.8 0.3 1.1
Warsaw 7.6 8.2 -0.6 0.9
Washington 7.7 6.4 1.3 1.2
West Lafayette 5.7 5.7 0.0 1.0
Wheatfield 6.2 5.9 0.3 1.1
Whitestown 8.8 7.1 1.7 1.2
Winamac 6.7 6.2 0.5 1.1
Mean 6.6 6.4 0.2* 1.0
Median 6.2 6.3 0.3 1.0
Range 4.8 to 9.7 4.9 to 8.4 -1.3 to 1.9 0.8 to 1.3
L-moments < Huff-Angel 21 (51%)
L-moments = Huff-Angel 1 (2%)
L-moments > Huff-Angel 19 (46%)
*This value does not match the one in table 6 because the amounts are to one decimal place in this table.
Figure 6. Correlation between L-moments and Huff-Angel methodsfor 2-, 5-, 10-, 25-, 50-, and 100-year recurrence intervals, and 24-hour rainfall amounts
13
Figure 7. Curve-fitting comparisons for 1-day amounts
agreement with the L-moments method than with the maxi-mum likelihood method. The correlations also remain higherfor the 50- and 100-year recurrence intervals than those forIndiana, indicating a better agreement at the longer intervals.
The mean 24-hour, 100-year values for the Huff-Angel,L-moments, and maximum likelihood methods are 5.81, 5.80,and 5.70 inches, respectively. Using a simple Analysis ofVariance (ANOVA) model, there are no significant differ-ences in the three methods. The histograms of the 100-yearvalues (figure 10) indicate that the Huff-Angel method moreclosely approximates a normal distribution than either of theother two methods. Overall, there is less skewness than in theIndiana data. The Huff-Angel method yielded larger values in10 cases (40 percent), and smaller values in 12 cases(48 percent), compared to the L-moments method. The twomethods agreed in 3 cases (12 percent), as shown in table 9.
The differences between the methods are generally nei-ther statistically significant nor (more importantly) meteoro-logically significant. As in Indiana, there are no systematicbiases between the methods except at the longest return period(100 years).
1 4
Figure 8. Histogram comparisonsfor 1-day rainfall amounts in Indiana
Figure 9. Comparison of Huff-Angel and L-moments methods for 100-year, 24-hour rainfall in Indiana
Table 8. Comparison of Three Methodsfor Estimating 24-Hour Maximum Amounts at Selected Return Periods for Minnesota
Huff-Angel vs. Huff-Angel vs. Maximum likelihoodMaximum likelihood L-moments vs. L-moments
Return Mean Corre- Mean Corre- Mean Corre-period difference lation difference lation difference lation
(inches) (inches) (in.)
2 0.00 0.94 0.01 0.94 0.01 0.99
5 0.05 0.97 0.05 0.95 0.00 0.98
10 0.11 0.97 0.09 0.92 -0.02 0.96
25 0.14 0.93 0.09 0.89 -0.05 0.93
50 0.15 0.89 0.07 0.85 -0.08 0.90
100 0.11 0.83 0.01 0.80 -0.10 0.87
15
16
Figure 10. Histogram comparisons for 1-day rainfall in Minnesota
17
Table 9. Performance of Huff-Angel and L-moments Methodsat the 24-Hour, 100-year Recurrence Interval for 25 Stations in Minnesota
Site (a) Huff-Angel (b) L-moments Difference Ratio (a):(b)
Baudette 5.9 4.8 1.1 1.2
Bird Island 5.4 5.4 0.0 1.0
Canby 4.2 5.5 -1.3 0.8
Cloquet 4.7 5.7 -1.0 0.8
Crookston 6.8 6.0 0.8 1.1
Detroit Lake 5.7 4.9 0.8 1.2
Grand Marais 4.2 3.7 0.5 1.1
Grand Meadow 7.0 7.1 -0.1 1.0
Grand Rapids 5.8 5.4 0.4 1.1
Hallock 6.4 5.8 0.6 1.1
Itasca 6.7 6.0 0.7 1.1
Little Falls 5.7 5.7 0.0 1.0
Minneapolis-St. Paul 7.1 6.5 0.6 1.1
Mora 3.9 4.4 -0.5 0.9
Morris 6.5 6.4 0.1 1.0
Pine River Dam 5.0 5.4 -0.4 0.9
Redwood Falls 4.0 4.0 0.0 1.0
Virginia 5.9 6.1 -0.2 1.0
Wadena 5.8 5.9 -0.1 1.0
Waseca 6.4 6.0 0.4 1.1
Willmar 6.2 7.2 -1.0 0.9
Winnebago 7.0 7.1 -0.1 1.0
Winona 5.3 6.0 -0.7 0.9
Worthington 7.0 7.1 -0.1 1.0
Zumbrota 6.7 7.1 -0.4 0.9
Mean 5.8 5.8 0.0 1.0
Median 5.9 5.9 0.0 1.0
Range 3.9 to 7.1 3.7 to 7.2 -1.3 to 1.1 0.8 to 1.2
L-moments < Huff-Angel 10 (40%)
L-moments = Huff-Angel 3 (12%)
L-moments > Huff-Angel 12 (48%)
3. FREQUENCY DISTRIBUTIONS OF HEAVY RAINFALL EVENTS
In our nine-state study, we have used two methods ofdata analysis and presentation of results: isohytal maps andareal averages. Both methods have advantages and disadvan-tages, but together they provide adequate information for thevaried needs of users. Descriptions of the two methods andthe results obtained are presented in this section.
A major problem encountered was how to develop thefrequency relations to provide maximum accuracy and reli-ability for the user. As indicated previously, a major source ofsampling error results from poor raingage exposure, inad-equate gage maintenance, plus human-induced errors duringdata entry and data reduction. Nonrepresentative spatialvariability may be introduced by rarely experienced severerainstorms (outlier events), which do not properly reflect theaverage frequency distribution expected within the 100-yeartime frame covered by this study. While the time distributionanalysis may eliminate outliers with respect to that station, itis much harder to remove systematic biases such as poorexposure or improperly maintained equipment. In both theisohyetal maps and the areal averages, every effort was madeto minimize these types of errors.
Point Rainfall Frequency DistributionsMost frequency relations in the past have used isohyetal
maps to present the frequency distributions (Yarnell, 1935;Hershfield, 1961). Although this approach can be susceptibleto considerable subjectivity and sampling errors, it is usefuland familiar to most users. It also facilitates accounting forsmaller-scale features in water-control design processes.Examples of small-scale features are increased rainfall founddownwind of large urban areas (Huff and Changnon, 1973)and changes associated with small-scale geographical fea-tures such as the hills of southern Illinois (Huff et al., 1975).In the nine-state project, only observations supported by twoor more stations have been incorporated into the analyses.
For each state, isohyetal patterns of point rainfall weredeveloped for the recurrence intervals and rainfall periodsindicated earlier (2-year to 100-year; 1-hour to 240-hours).Several variables were used in establishing these patterns: 1)the frequency relations derived for each precipitation stationfrom the recorded data at that station; 2) climatological-meteorological knowledge of the regional precipitation char-acteristics; and 3) known effects of physiographic features,inadvertent weather modification factors, or both withinvarious regions of the state.
Initially, maps were plotted for each selected recur-rence interval and rainfall duration from the individual stationfrequency distributions. Based strictly on the station data,isohyetal patterns were then lightly sketched to reveal areaswhere pattern distortions occur. These distortions are mostoften due to natural and/or human-induced variability (dis-cussed earlier). At this point, consideration of variables (2)and (3) above becomes important in adjusting the isohyetal
18
patterns to overcome unrealistic precipitation differences thatmay occur within areas of approximately homogeneous pre-cipitation climate. But care must be taken not to overlook realspatial variations related to physiographic and inadvertentweather factors.
For example, in our Illinois pilot study, the isohyetalpatterns downwind of the St. Louis metropolitan area showedan expected increase in the occurrence of heavy rainstorms.This was a real variation as opposed to a variability distortion(Changnon et al., 1977). An increase in the frequency dis-tribution of heavy storm events in western Illinois wasdetermined to be real and related to a well-recognized thun-derstorm breeding area in the Missouri Ozarks located to thesouthwest (upwind of the high identified in the isohyetalanalyses). Similarly, climatic variations produced substantialchanges in the heavy rainstorm distribution characteristicsfrom north to south in the state. There were also several areas,however, in which no real cause could be found for substantialdifferences in precipitation within relatively short distances.These differences were considered sampling vagaries (unrealvariation) and the isohyetal pattern was adjusted to agree withthe distributions indicated by other stations in the surroundingregion. In summary, we believe that careful attention tovariables (1), (2), and (3) will produce logical, reliableisohyetal patterns that closely approximate the true distri-bution characteristics of heavy rainstorms within each stateand for the nine-state region. This analytical philosophy wasfollowed throughout the study.
The adjusted isohyetal patterns resulting from theforegoing analytical procedures are shown in the maps in part2 of this report for selected recurrence intervals ranging from2 years to 100 years and rainfall periods varying from 1 hourto 10 days. To determine frequency values for rainfall periodsof less than 1 hour, recurrence intervals of less than 2 years,or both, tables 3 and 4 in chapter 1 provide information forcomputing amounts for rain periods as small as 5 minutes andrecurrence intervals as short as 2 months. Note that isohyetsextending over the Great Lakes are for maintaining continuityand may not reflect actual conditions over the lakes.
The isohyetal gradient sometimes varies appreciablybetween consecutive maps in part 2. This was necessary tomaintain proper display of the spatial pattern characteristics(highs, lows, troughs, ridges, etc.) indicated by the data. It wasconsidered pertinent to show all features of the isohyetalpatterns that persisted throughout all or most of the stormdurations and recurrence intervals provided in the map series.These features reflect the combined effects of precipitationclimate and other factors such as topography (hills, valleys,and large water bodies) and urban influences.
Areal Mean Frequency DistributionsAnother approach to the spatial distribution problem is
a method used by Huff and Neill (1959) in an earlier Illinois
study to alleviate the consequences of spatial variability. Thestate was divided into regions of approximately homogeneousclimate with respect to heavy rainstorm events. Averagerelations were then developed for each division. In ourMidwest study, however, consideration of available climateinformation on the distribution of heavy rainfall andclimatological-meteorological knowledge of storm systemcharacteristics indicated that the well-established NWS cli-matic divisions could be used to divide the states. The onlyexception was Illinois, where a slight change was made in theestablished divisions to more accurately reflect a combinedeffect from the Ozarks and the Mississippi River valley in thewestern part of the state. While NWS climate division aver-ages are recommended by the authors for most purposes,hydrologists often prefer to use isohyetal maps (when workingwith basins that cover two or more climate divisionsfor example).
The foregoing technique does not eliminate the poten-tial sampling errors in the data samples, but it does moderatetheir effect in regions of similar precipitation climate, andshould produce better estimates of the true distribution ofheavy rainstorms across the nine-state region. Unless the
divisions are properly selected, however, the averaging tech-nique may mask real small-scale effects, such as those in-duced in the vicinity of the Great Lakes or the MissouriOzarks. This problem would become more acute in regionsincorporating major changes in topography, such as theRocky Mountain and Appalachian regions.
Frequency relations for sectional mean rainfall for eachstate are shown in the sectional mean frequency distributiontables in part 2. Rainfall values are provided for recurrence in-tervals ranging from 5 minutes to 10 days, and for recurrenceintervals varying from 2 months to 100 years in each section.
Use of the tables is indicated by the following examplefor Indiana (table 2 in part 2). Assume a user wishes todetermine the 24-hour rainfall amount expected to occur, onthe average, of once in 25 years at a given location in theNorthwest Climate Section. Move down the duration columnto 24 hours, which corresponds to 5.22 inches in the 25-yearcolumn. This is the average 25-year amount for the section.In a specific 25-year period, however, this value may varysomewhat between individual points due to random spatialvariability within the relatively homogeneous precipitationclimate of the section.
19
4. TIME DISTRIBUTIONS OF RAINFALL IN HEAVY STORMS
Modern runoff models used in the design of urban andsmall-basin water-control structures, necessitate defining thetime distribution characteristics within heavy rainstorms.Such information is also pertinent to the use of the frequencydistributions presented in this report. Huff (1990) used datafrom long-term operation of three recording raingage net-works in Illinois to develop time distribution relationships.Although based upon Illinois data, these relationships shouldbe applicable to our nine-state region and other locations ofsimilar precipitation. The Illinois study was undertaken be-cause earlier time distribution models, developed by the SoilConservation Service (1972) and others, were not consideredsatisfactory for use in the Midwest’s heavy rainstorms.
Method and Results of AnalysisThe time distributions were expressed as cumulative
percentages of storm rainfall and storm duration to enablevalid comparisons between storms and to simplify analysesand presentation of data. Relations were developed for pointrainfall and for areas of 10 to 400 square miles. Areal group-ings showed only small changes in the time distributionswith increasing sampling area. Therefore, average relationswere determined for point rainfall and for areas of 10 to 50and 50 to 400 square miles. Rainfall distributions weregrouped according to whether the heaviest rainfall occurredin the first, second, third, or fourth quarter of a storm. Foreach quartile grouping, a family of curves was then derivedto provide a quantitative measure of the interstorm variabilityexpected to occur within that group. The interstorm variabil-ity was then expressed in probability terms for userapplication.
Tables 10-12 have been abstracted from the Huff report(1990). Table 10 shows the median time distribution of heavystorm rainfall at a point, table 11 for areas of 10 to 50 squaremiles, and table 12 for areas of 50 to 400 square miles. Thesetables show cumulative percent of rainfall expressed asa function of the cumulative percent of total storm time(storm duration) for first-, second-, third-, and fourth-quartile storms.
The median distributions are most commonly used byhydrologists and others. The reader is referred to Huff (1990)for additional information on time distributions for probabil-ity levels ranging from 10 to 90 percent. For example, table13, assembled from the families of curves provided in thereferenced report, shows time distributions at the 10, 50, and90 percent probability levels in first-quartile storms for areasof 50 to 400 square miles. The 10 and 90 percent distributionsare useful for estimating runoff relations in the more extremetypes of time distributions.
Application of ResultsFor mean rainfall on small basins (≤ 400 square miles),
the first- and second-quartile storms were found to be most20
prevalent (33 percent each), followedby third-quartile storms(23 percent), and fourth-quartile storms (11 percent). Forpoint rainfall, first-quartile storms were most prevalent (37percent), followed by second-quartile storms (27 percent),third-quartile storms (21 percent), and fourth-quartile storms(15 percent).
Storms with durations of 6 hours or less, 6.1 to 12hours, 12.1 to 24 hours, and greater than 24 hours tended to beassociated with first-, second-, third-, and fourth-quartiledistributions, respectively.
For most structural design applications, use of thequartile type occurring most often is recommended for thedesign duration under consideration. For example, use thefirst-quartile curves for design durations of 6 hours and less,and use the second-quartile distributions for designs involv-ing storm durations of 6.1 to 12 hours.
Using Results in Structural Design Problems:Case Studies
Case OneFirst, assume that a design based on a 5-inch rainstorm
of 6-hour duration is being determined for a given point,based on a median or average time distribution. In this case,a first-quartile median curve would be appropriate. Then,from table 10, one can determine that 33 percent of the rain-fall total (1.67 inches) would occur in the first 10 percent(36 minutes) of the storm. Similarly, 60 percent (3.00 inches)would be expected to occur in the first 25 percent (90 minutes)of the storm, and 82 percent (4.10 inches) in the first 50 percent(3 hours) of the rain period.
Case TwoNow, assume that the same design problem involves
a 5-inch, 6-hour storm on a basin encompassing 100 squaremiles. Then, refer to table 12. In this case, the medianvalues indicate that the areal average would be 17 percent ofthe rain (0.85 inch) in the first 10 percent (36 minutes) of afirst-quartile storm. During the first 25 percent of the storm(90 minutes), 63 percent of the rain (3.15 inches) would fall,and during the first 50 percent (3 hours), 86 percent(4.30 inches) would fall.
Case ThreeIf a second-quartile instead of a first-quartile storm
were used as the design basis for the 100-square-milearea, only 4 percent (0.20 inch) would occur in the first10 percent of the storm. This would increase to 21 percent(1.05 inches) in the first 25 percent (90 minutes) of thestorm period.
Then a rapid increase to 73 percent of the totalrainfall (3.65 inches) would occur by the halfway point ofthe storm event.
21
Table 10. Median Time Distributions of Heavy Storm Rainfallat a Point
Cumulative storm rainfall (percent) for given storm type
Cumulativestorm time (percent)
First-quartile
Second-quartile
Third-quartile
Fourth-quartile
5 16 3 3 2
10 33 8 6 5
15 43 12 9 8
20 52 16 12 10
25 60 22 15 13
30 66 29 19 16
35 71 39 23 19
40 75 51 27 22
45 79 62 32 25
50 82 70 38 28
55 84 76 45 32
60 86 81 57 35
65 88 85 70 39
70 90 88 79 45
75 92 91 85 51
80 94 93 89 59
85 96 95 92 72
90 97 97 95 84
95 98 98 97 92
22
Table 11. Median Time Distributions of Heavy Storm Rainfallon Areas of 10 to 50 Square Miles
Cumulative storm rainfall (percent) for given storm type
Cumulativestorm time (percent)
First-quartile
Second-quartile
Third-quartile
Fourth-quartile
5 12 3 2 2
10 25 6 5 4
15 38 10 8 7
20 51 14 12 9
25 62 21 14 11
30 69 30 17 13
35 74 40 20 15
40 78 52 23 18
45 81 63 27 21
50 84 72 33 24
55 86 78 42 27
60 88 83 55 30
65 90 87 69 34
70 92 90 79 40
75 94 92 86 47
80 95 94 91 57
85 96 96 94 74
90 97 97 96 88
95 98 98 98 95
23
Table 12. Median Time Distributions of Heavy Storm Rainfallon Areas of 50 to 400 Square Miles
Cumulative storm rainfall (percent) for given storm type
Cumulativestorm time (percent)
First-quartile
Second-quartile
Third-quartile
Fourth-quartile
5 8 2 2 2
10 17 4 4 3
15 34 8 7 5
20 50 12 10 7
25 63 21 12 9
30 71 31 14 10
35 76 42 16 12
40 80 53 19 14
45 83 64 22 16
50 86 73 29 19
55 88 80 39 21
60 90 86 54 25
65 92 89 68 29
70 93 92 79 35
75 95 94 87 43
80 96 96 92 54
85 97 97 95 75
90 98 98 97 92
95 99 99 99 97
24
Table 13. Time Distributions of Areal Mean Rainfall on 50 to 400 Square Milesin First-Quartile Storms at Probability Levels of 10, 50, and 90 Percent
Cumulative storm rainfall for given storm probability
Cumulativestorm time (percent)
10 percent 50 percent 90 percent
5 24 8 2
10 50 17 4
15 71 34 13
20 84 50 28
25 89 63 39
30 92 71 46
35 94 76 49
40 95 80 52
45 96 83 55
50 97 86 57
55 98 88 60
60 98 90 63
65 98 92 67
70 99 93 72
75 99 95 76
80 99 96 82
85 99 97 89
90 99 98 94
95 99 99 97
5. SEASONAL DISTRIBUTIONS OF HEAVY RAINFALL
BackgroundIn the design of some hydrological systems or struc-
tures, it is pertinent to know the seasonal characteristicsof heavy rainstorms as well as the frequency distributions ofmaximum storm rainfall amounts for various storm durations.For example, when the soil is near saturation, a spring stormof intensity equivalent to a 5-year recurrence interval mayhave different consequences than had the same stormoccurred in a drier summer month. Winter storms, while gen-erally producing less precipitation than summer storms, canbe devastating if they occur over frozen ground. With orwithout snow cover, these winter storms can cause rapidflooding. Heavy rainfall storms in the early spring and late fallmay lead to higher rates of erosion due to tillage practicesand a lack of vegetative cover.
Unfortunately, a lack of resources prohibited an exten-sive analysis of seasonal rainfall frequencies, comparableto the annual analysis. This report, however, presents threestudies by the authors to provide some insight regarding thebehavior of heavy rainstorms across the seasons inthe Midwest.
Analysis and ResultsThe studies used the traditional four seasons: winter
(December-February), spring (March-May), summer (June-August), and fall (September-November).
Seasonal PrecipitationPrior to a discussion of heavy rainstorms, it is helpful to
understand the seasonal change in temperature and precipita-tion in the Midwest. In general, summer in the Midwest isnot only the warmest season, but also the wettest one. MeanJuly precipitation ranges from 5.2 inches in Kentucky to2.4 inches in Michigan. By contrast, mean January precipita-tion ranges from 4.0 inches in Kentucky to 0.6 inches inMinnesota. The largest differences in precipitation occur inwinter, with the northern states (Minnesota, Wisconsin, andMichigan) receiving 50 to 80 percent less precipitation thanin summer. Table 14 shows the annual precipitation and per-cent contribution by season. In general, Kentucky is thewettest state in the region with a nearly uniform distributionof precipitation throughout the four seasons. Minnesota is thedriest state with 42 percent of its precipitation falling insummer and only 9 percent in winter. For all nine statescombined, summer provides the largest contribution,followed by spring, fall, and winter with 32,27,25, and 16percent, respectively.
In the Midwest, the temperatures for all four seasons andthe annual mean temperature decrease northward (table 15). Ingeneral, Kentucky is the warmest state while Minnesota is thecoldest state. In winter, Kentucky is the warmest state withmuch of its precipitation falling as rain rather than snow.
Seasonal Distribution of Heavy RainstormsThe number of heavy storms in the Midwest changes
from season to season as well as from state to state. Table 16shows the seasonal contribution of the top-ranked, 1-daystorms for 275 stations in the region. Three-fourths of suchstorms occur in the summer. The summertime maximum ismost pronounced in Minnesota, Iowa, Illinois, Indiana, andWisconsin. This is probably due to the shorter convectiveseason in the northern latitudes. In Michigan, Missouri, andOhio, the fall also contributes a significant number of storms.It is not known what physical process causes this effect inthese three geographically diverse states. Missouri is alsonoteworthy in that 13 percent of its heavy storms occur inwinter. Overall, the largest number of storms occur in June,July, August, and September.
Similar results were found for the top-ranked 2-, 3-,5-, and 10-day totals. In each case, the largest percentage ofstorms occurred in the summer. A significant number of thestorms in Kentucky, however, occurred in winter for theselonger durations. Similar results were also obtained for all theannual maximum storms at each station. Summer continuedto be the season of most frequent occurrences although thepercentages were closer to 50 percent of the total (comparedto 75 percent for the top-ranked storms). The contributions inspring and fall were from 20 to 25 percent of the total.
While summer is the dominant season for the Midwestfor 1-day storms, an analysis of the top-ranked storms occur-ring in the warm season (April - September) compared withthe cold season (October - March) indicates that the southern-most part of the region is more likely to experience its heavieststorms in the cold season at longer durations. This featurebecomes most predominant in the 10-day storms. The top-ranked 10-day storms (figure 11) occur mostly in the coldseason for the southern half of Missouri and for a large,coherent region along the Ohio River valley. These events areprobably associated with the synoptic-scale cyclones that passthrough those regions during those months. This pattern is notfound for the 1-day storms, but becomes increasingly evidentfor the 2-, 3-, and 5-day storms. Because this pattern onlyoccurs in the southern portions of Missouri, Indiana, and Ohio,it is not discernible in the state values in table 16.
In general, more of the heavier storms occurred insummer than in any other season, while the least number oc-curred in winter for shorter durations. In states with a shorterconvective season (e.g., Minnesota), the peak in summer wasmore prominent than in regions where substantial convectiveactivity may occur throughout the year (e.g., Kentucky).
Rainfall Frequencies by SeasonThe seasonal distribution of storms suggests that the
magnitude of the seasonal rainfall frequency curves maychange as one moves northward. For example, in the southernregion where the number of storms is more evenly distributed
25
26
Table 14. Seasonal Rainfall Distribution (inches) for 1961-1990 by State
StateAnnual rainfall Winter
(percent)Spring
(percent)Summer(percent)
Fall(percent)
Illinois 38.69 16.7 29.1 29.8 24.3
Indiana 40.59 18.8 28.9 29.1 23.1
Iowa 33.08 9.2 28.2 38.1 24.6
Kentucky 48.15 23.6 28.6 25.9 22.0
Michigan 32.17 17.4 24.0 30.0 28.6
Minnesota 26.63 8.7 25.1 41.8 24.5
Missouri 41.15 15.8 29.4 28.5 26.2
Ohio 38.14 19.1 28.1 29.8 23.0
Wisconsin 31.68 11.1 25.4 36.6 26.9
Midwest 36.70 15.6 27.4 32.2 24.8
Table 15. Seasonal Temperature Distribution (°F) for 1961-1990 by State
State Annual Winter Spring Summer Fall
Illinois 51.6 27.2 51.5 73.3 54.2
Indiana 51.4 28.3 51.0 72.2 53.9
Iowa 47.6 20.7 48.0 71.7 50.2
Kentucky 55.3 34.7 55.1 74.1 57.0
Michigan 44.2 20.7 42.3 66.0 47.6
Minnesota 40.8 11.0 41.3 66.9 43.9
Missouri 54.3 31.3 54.4 75.2 56.2
Ohio 50.3 28.3 49.6 70.6 53.0
Wisconsin 42.9 16.0 42.7 66.8 46.1
Midwest 48.7 24.2 48.4 70.8 51.4
State
Illinois
Indiana
Iowa
Kentucky
Michigan
Minnesota
Missouri
Ohio
Wisconsin
Midwest
Table 16. Seasonal Contribution of Top-Ranked 1-Day Storms
Winter Spring Summer Fall Most frequent(percent) (percent) (percent) (percent) month
3.3 20.0 65.0 11.7 July
2.4 17.1 63.4 17.1 July
0.0 2.3 72.1 25.6 August
4.0 20.0 52.0 24.0 June
2.3 14.0 39.5 44.2 September
0.0 7.7 80.8 11.5 August
13.0 10.9 41.3 34.8 September
4.9 14.6 48.8 31.7 July, September
0.0 5.0 76.7 18.3 August
3.4 12.2 60.3 24.2 July, August
across the seasons, one would expect similar seasonal rainfallfrequency curves. In the northern region where there is anoticeable summer maximum in heavy storms, one wouldexpect the summer rainfall frequency curves to be muchhigher than those for the other seasons. To examine thisfurther, a transect was drawn from southeast Kentucky tonorthwest Minnesota, and 12 stations were chosen at approxi-mately equal intervals (figure 12). Seasonal time series weredeveloped for the period 1949-1990 (42 years) for all fourseasons. The 1-day rainfall frequency amounts were calcu-lated for 2-, 5, 10-, 25-, and 50-year recurrence intervalsusing the maximum likelihood method and the GeneralizedExtreme Value (GEV) distribution (Farago and Katz, 1990).
For each station, the four rainfall frequency curves areplotted for comparison (figure 13). The stations are arrangedfrom north to south. It is readily apparent that the wintertimefrequency curves are much lower than the other frequencycurves in the northern states. At the same time, the summer-time curves are nearly equal to the annual curves in the north.At the southern stations, the seasonal curves are closertogether, and the summertime curves are not as close to theannual curves.
To generalize these findings, the ratio of the values ofthe seasonal curves to the annual curves was calculated for allrecurrence intervals. Because the ratio did not change signifi-cantly from one recurrence interval to the next, an average forall recurrence intervals was calculated for each season at eachstation. These ratios are expressed as percentages in figure 14.
Keeping in mind that a lower station number represents higherlatitudes, the percentage of the winter values to annual valuesis very low in Minnesota, and increases dramatically to thesouth. A similar effect is noticeable, although not as pro-nounced, in the relationship for spring. On the other hand, thepercentages of summer and fall values show little change withlatitude. The summer percentages are all within 20 percent ofthe annual values.
In practice, the seasonal distribution of heavy rainfallmay become important with respect to antecedent soil mois-ture conditions. For example, the USDA Soil ConservationService (1968) discusses three categories of antecedent mois-ture conditions (AMC):
Type I. Soils are dry but not to the wilting point or whenplowing or cultivation occurs (typical at times in spring,summer, and fall).
Type II. Average conditions precede previous annualmaximum floods (the average case).
Type III. Heavy rainfall or light rainfall and low tem-peratures have occurred during the last five days prior to thegiven storm, and the soil is nearly saturated (particularly inlate fall to early spring, especially in the southern third of theMidwest region).
Assuming a soil with a moderate infiltration rate (hy-drological soil group B), the curve number for a row crop(straight row) can range from 61 to 90 (see table 17). This
27
Figure 11. Top-ranked 10-day storms by season(1 = cold, 0 = warm)
Table 17. Curve Number (CN) and Runoff(Q) Valuesfrom Three Antecedent Moisture Conditions (AMC)
for Row Crops
AMC CN Q (inches)
I 61 2.0
II 78 3.8
III 90 5.4
wide range of curve numbers can lead to large differences inrunoff. Assuming a 6-inch, 24-hour, 100-year rainfall, thecalculated direct runoff can range from 2.0 to 5.4 inches,depending on the antecedent soil conditions (table 17). There-fore, in places with typically heavy winter precipitation (Ken-
28
Figure 12. Stations used in comparing seasonalvariations in frequency curves
tucky), AMC type III can lead to much higher runoff valuesthan by using the annual amounts and AMC type II.
SummaryDifferences in the seasonal rainfall frequency values
are evident due to differences in the seasonal contribution ofheavy rainstorms. In the Midwest, these differences can bequite significant in the northern states where summer precipi-tation dominates. In the southern states, significant heavystorms occur in all seasons. For example, in southern Mis-souri, Indiana, and Ohio, the heaviest amounts for the longerduration storms (5-day and 10-day) are most likely to occur inthe cold season. The impacts of all heavy rainstorm events willvary from season to season, depending on soil moisture, stateof the soil (frozen or nonfrozen), and vegetative cover.
Figure 13. Seasonal rainfall frequency curves
29
Figure 13. Concluded
30
Figure 14. Ratio of seasonal curve to annual curve amounts expressed in percentages
31
6. FLUCTUATIONS IN FREQUENCY DISTRIBUTIONSOF HEAVY RAINSTORMS IN THE MIDWEST
BackgroundHeavy rainfall events are important in the design of
water-related structures (e.g., storm sewer systems), in agri-culture, in weather modification, and in monitoring climatechange. Traditionally, hydrometeorologists have fit variousstatistical distributions to historical precipitation data toderive the recurrence intervals for selected storm durations.The assumption underlying the derivation of these values hasbeen that there are year-to-year variations in the precipitationrecord, but the time series is stationary without major tempo-ral fluctuations or long-term trends during the typical designlife (50 to 100 years for most water-related structures). Thisassumption allows the use of all available historical data withequal weight. However, a preliminary study of Illinois by Huffand Changnon (1987) using 1901-1980 data for 22 stationsinvestigated the possibility of a climatic trend in the distribu-tion of heavy rainstorms in Illinois. A comparison of l-dayand 2-day rainfall amounts for 2-year to 25-year recurrenceintervals showed significant changes in the northerntwo-thirds of the state for two 40-year periods (1901-1940 and194l-1980). This was supported by an earlier study of Illinoisclimate fluctuations (Changnon, 1984), which showed sizableshifts in total precipitation and thunderstorms for 1901-1980.
Analytical Approach and Results
IllinoisIn Illinois, a 61-station sample was used to investigate
the properties of the frequency distribution of maximum 24-hour and 48-hour storms derived from two 40-year periods(1901-1940 and 1941-1980). The frequency distributionswere derived from the partial duration series of rainstorms foreach station. The frequency values were obtained from log-log curves derived for each station. The l-day and 2-dayvalues obtained were converted to maximum 24-hour and 48-hour amounts using the transformation factors 1.13 and 1.05,respectively, derived by Hershfield (1961) and Huff andNeill (1959).
The change between the two periods was expressed interms of the ratio of values from the 1941-1980 period to thosefor the 1901-1940 period. A value > 1 indicates an increase inintensity, and a value < l indicates a decrease in intensity fora given storm duration and recurrence interval.
The results of the expanded study in Illinois supportedthe findings of Huff and Changnon (1987). For the two 40-year periods, there is a general increase in the northern two-thirds of the state and a slight decrease in the southernone-third. Figure 15 shows the pattern of ratios (1941-1980/1901-1940) calculated for 24-hour, 2-year rainfalls derivedfrom station frequency curves based on data for each 40-yearperiod. The pattern in the figure holds for the 5-year, 10-year,
32
and 25-year recurrence intervals at the 24-hour and 48-hourstorm durations (Huff and Angel, 1989). The ratio for the two40-year periods had similar spatial behavior for the two stormdurations. Within the state, 62 percent of the stations hadratios exceeding 1.00, and 36 percent exceeding 1.10 for24-hour storms with a recurrence interval of 2 years. For a2-year, 48-hour rainstorm, 68 percent of the stations hadratios exceeding 1.00,40 percent exceeding 1.10. The datawere inadequate to derive 50-year and 100-year ratios fromthe station frequency curves.
The results for the two 40-year periods are supported byother studies in Illinois. In a study of the 1901-1980 period,Changnon (1985) found gradual changes to a wetter regimein Illinois that was most pronounced in the last 15 years(1965-1980). In an earlier study, Changnon (1983) notedincreased flooding in recent years. especially in northeasternIllinois. This agrees with the 20 to 40 percent increase in theheavy rainfall distribution for northeastern Illinois found inour study.
Table 18 illustrates the effect of climatic variationsbetween the two 40-year periods on heavy rainstormfrequency distributions (Huff and Angel, 1990). For 24-hourmaximum rainfall at average recurrences of 2,5, and 10 years,six stations were selected to reflect different degrees ofchange during the 80-year sampling period. At Rockford in
Figure 15. Ratio for two 40-year periods (1941-1980and 1901-1940) for 2-year, 24-hour storms
Table 18. Examples of Variation in Recurrence Intervals Indicatedfor Maximum 24-Hour Rainfall Between Frequency Curves
Derived from 1901-1940 and 1941-1980 Data in Illinois
Recurrenceinterval(1941-1980curves)
2
5
10
Rockford
(NW)
5
15
35
Equivalent Recurrence Intervalfor Selected Locations (1901-1940 curves)
Kankakee Quincy Peoria Effingham Belleville(E) (W) (C) (ESE) (SW)
5 5 3+ 3+ 2-
16 13 9 19 4
35 27 17 21 8
northern Illinois, the 2-year value on the frequency curvederived from 194l-1980 data corresponds to the 5-year valueon the 1901-1940 curve. Similarly, the 5-year amount esti-mated by the 1941-1980 curve corresponds to the 15-yearamount on the 1901-1940 curve, and the 10-year valuecorresponds to the 35-year value. If a structure with a 70-yearlifetime was designed for rainstorms with a 35-year recur-rence interval using the 1901-1940 data it would be implicitlyexpected to be exposed to only two such storms. The 1941-1980 data, however, suggests it might be exposed seven timesto a storm of such magnitude over 70 years (on average). Theunderestimates would be most pronounced in northern andwestern Illinois (Rockford, Kankakee, and Quincy).
The MidwestThe next step was to extend the analyses to the
neighboring states in the Midwest to determine if the patterncontinued outside Illinois. For the other eight states (Indiana,Iowa, Kentucky, Michigan, Minnesota, Missouri, Ohio, andWisconsin), analyses similar to that employed in Illinois wereused for comparisons between two 40-year periods (1907-1946 and 1947-1986). The Illinois values were also adjustedto this time frame. Comparisons were made for selectedrecurrence intervals for 24-hour storms. Unfortunately, thenumber of long-term stations with digital records is less in theother states than in Illinois. Whereas 61 stations were avail-able for Illinois, records for only 24 stations were available ontape for Indiana, 35 for Iowa, 12 for Kentucky, 39 for Mich-igan, 24 for Minnesota, 27 for Missouri, 15 for Ohio, and 13for Wisconsin. Thus the spatial detail and accuracy is dimin-ished somewhat in these other states (especially Ohio andWisconsin). Figure 16 shows the stations used in this section.
Figure 17 shows the 40-year ratios for 2-year, 24-hourstorms. The shaded areas show regions with ratios > 1.00;that is, an increase in rainfall amounts for a given frequencyand duration. There is a large area of increased valuesthroughout the region. Decreased amounts are indicated inMissouri, Wisconsin, and along the Ohio River valley. Thereis some degree of spatial coherency in the area of increased
values. That is, these are not just isolated, random pockets ofhigh values. The areas with ratios > 1.10 (10 percent), amore significant threshold, show a narrow band starting nearSt. Louis and continuing to the northeast through Illinois,northwest Indiana, and into lower Michigan. Minnesota alsohas a larger area through the northwestern portion of the statewith significantly higher ratios, There are other small pocketsof high values in Iowa, Missouri, and Kentucky. Thesesmaller regions may have been caused by smaller-scaleeffects, such as one exceptionally heavy rainstorm.
The map of 40-year ratios for a 5-year, 24-hour stormshows a pattern similar to the 2-year, 24-hour map for ratios> l.00 (figure 18). This coherence between return periods isconsistent with the results found in Illinois. The areas of 5-year, 24-hour storm ratios > l.10 (10 percent) show the sameband as before with an extension into northern Ohio as wellas an area in northwest Missouri. The values within this bandare also more intense. For example, the ratios near Chicagoare 25 to 40 percent on the 5-year, 24-hour map, but only 20percent on the 2-year, 24-hour map. The similar appearancebetween the 2- and 5-year, 24-hour maps suggests the changesare due to something other than sampling vagaries. Thepatterns of the 10-year, 24-hour analysis (not shown) weresimilar to those for the shorter intervals.
To investigate whether the patterns are indeed true andnot due to random noise, correlations were calculated be-tween the ratio maps and two random patterns, which weregenerated by randomly shuffling the station locations for the2-year, 24-hour data. Thus the data values are the samealthough their locations are different. Table 19 shows theresults of the correlation analysis. Although there is highcorrelation among the real patterns, no relationship was foundfor the random patterns.
Table 20 shows the percentage of stations having ratios≥1.00 and≥1.10. In general, two-thirds of the stations showedsome increase while one-third showed increases > 10 percentbetween the two 40-year periods.
33
Figure 16. Stations used in temporal change study
Figure 17. Ratio pattern for 2-year, 24-hour storms
Figure 18. Ratio pattern for 5-year, 24-hour storms
Summary and ConclusionsThis study has examined the change over time
of the heavy rainfall distribution in the Midwest. A de-tailed study in Illinois suggests an increase in rainfallamounts for 2-, 5, and 10-year recurrence intervals duringrecent years. This is supported by other temporal studiesof precipitation and related variables in Illinois. Preliminaryresults for other parts of the Midwest show a southwestto northeast axis of maximum change extending fromMissouri to Michigan and northern Ohio. The increasesappear to be greater than expected from natural cli-matic variability and are sufficiently large to haveimplications for water structure designs and other aspectsof applied climatology. Furthermore, the findings suggestthat the assumption of a stationary time series for fittingstatistical distributions to historical precipitation data maybe invalid. The results also suggest the need to update rain-fall frequency relations more frequently. An update on theorder of every 20 years would be appropriate to capture anysubstantial changes. It cannot be determined at this time whatunderlying physical processes may be involved withthese changes.
34
35
Table 19. Correlation Analysis Between the Three Real Maps and Two Random Maps
2-Year 5-Year 10-Year Random #1 Random #2
2-Year 1.00 0.76 0.53 -0.03 0.00
5-Year 0.76 1.00 0.90 -0.02 -0.04
10-Year 0.53 0.90 1.00 -0.03 -0.03
Random #1 -0.03 -0.02 -0.03 1.00 0.05
Random #2 0.00 -0.04 -0.03 0.05 1.00
Table 20. Percentage of Stations Showing Increased Precipitation Amountsat Selected Return Periods for 24-Hour Storms Between Two 40-year Periods
(1947-1986 and 1907-1946)
Ratio >1.00 Ratio >1.10
2-year 63 24
5-year 63 31
10-year 60 34
7. SPATIAL CHARACTERISTICS OF HEAVY RAINSTORMS IN THE MIDWEST
Data from dense raingage networks operated by theIllinois State Water Survey have supported numerous studiesof the spatial distribution characteristics of heavy rainstormssuch as those in this report. Key results from several of thesestudies have been abstracted from published reports andtechnical papers and included here for the convenience of theuser. They provide pertinent information for both hydrologi-cal designers and systems operators. Although based onIllinois data, the relationships are considered generally appli-cable to the Midwest.
Relation Between Point and Areal MeanRainfall Frequency
Knowledge of the frequency distribution of areal meanrainfall is pertinent to the efficient design of hydraulic struc-tures such as dams, urban storm sewers, highway culverts, andwater-supply facilities. In the United States, a relatively largeamount of data is available on the frequency distribution ofpoint rainfall, but there is little information on the frequencydistribution of areal mean rainfall. Consequently, there hasbeen a need to determine how the mean rainfall frequencydistributions for small areas about a point are related to thepoint frequency distributions.
Hershfield (1961) presented area-depth curves for esti-mating areal mean rainfall frequencies from point rainfallfrequencies. Information was provided for areas ≤400 squaremiles and for storm durations of 0.5 to 24 hours. The relationswere developed from limited raingage network data andapparently considered applicable throughout the United States.Huff (1970) used data from dense raingage networks inIllinois to provide similar relationships more applicable to theMidwest for storm durations of 0.5 to 48 hours. Results aresummarized in table 21.
Storm ShapeRunoff characteristics in heavy storms are influenced
by the shape and movement of the storms. Two studies havebeen made to determine the shape characteristics of heavyrainstorms in Illinois. In one study, data from 260 storms ona dense raingage network in central Illinois were used toinvestigate shapes on areas of 50 to 400 square miles (Huff,1967). Storms were used in which areal mean rainfall ex-ceeded 0.50 inch. In the other study, historical data for 350heavy storms having durations up to 72 hours were used in ashape study of large-scale, flood-producing rain events. Thesewere storms in which maximum l-day amounts exceeded 4inches or in which 2-day and 3-day amounts exceeded 5 inches(StoutandHuff, 1962).Storms encompassed areas that rangedfrom 200 to 10,000 square miles.
The study of historical storms indicated that the rainintensity centers most frequently had an elliptical shape. Theratio of major to minor axis tended to increase with increasingarea enclosed within a given isohyet; that is, the ellipsebecomes more elongated. Within limits employed in thestudy, no significant difference in the shape factor occurredwith increasing storm magnitude or with durations rangingfrom a few hours to 72 hours.
In the network study, elliptical patterns were found alsoto be the most prevalent type, but the heaviest storms tendedto be made up of a series of rainfall bands. Intensity centerswithin these bands, however, were most frequently elliptical.From these two studies, a mean shape factor was determinedthat can be used as guidance in hydrologic problems in whichstorm shape is a significant design factor. The shape curve isshown in figure 19 for areas of 10 to 1,000 square miles. Forthose interested, the curve can be continued to 10,000 squaremiles because storms up to this size were included in thehistorical storm study.
Table 21. Relation Between Areal Mean and Point Rainfall Frequency Distributions
Ratio of areal to point rainfall for given area (square miles)Storm period
(hours)10 25 50 100 200
0.5 0.88 0.80 0.74 0.68 0.62
1.0 0.92 0.87 0.83 0.78 0.74
2.0 0.95 0.91 0.88 0.84 0.81
3.0 0.96 0.93 0.90 0.87 0.84
6.0 0.97 0.94 0.92 0.89 0.87
12.0 0.98 0.96 0.94 0.92 0.90
24.0 0.99 0.97 0.95 0.94 0.93
48.0 0.99 0.98 0.97 0.96 0.95
3 6
400
0.56
0.70
0.78
0.81
0.84
0.88
0.91
0.94
Figure 19. Mean shape factor for heavy storms
Storm OrientationAn important consideration in any region is the orien-
tation of the major axis of heavy rainstorms. For example,if the axes of heavy rainstorms tend to be parallel to a riverbasin or other area of concern, then the total runoff in thisregion will be greater, on the average, than in a regionperpendicular to most storm axes. The orientation of the stormaxis also provides an indication of the movement of the majorprecipitation-producing entities embedded in any large-scaleweather system. Because most individual storm elementshave a component of motion from the west, an azimuth angleranging from 180 to 360° was ascribed to each storm. Thus, ifa storm had an orientation of 230°, the orientation was alonga line from 230 to 050° (southwest to northeast).
No significant difference was found between the orien-tation of storms when they were stratified according to meanrainfall and areal extent. Table 22 shows the distribution in260 heavy storms having mean rainfall exceeding one inchover a contiguous areas ≤10,000 square miles (Huff andSemonin, 1960). This distribution is considered typical for
Table 22. Orientationof Heavy Rainstorms
Azimuth Storms Azimuth Storms(degrees) (percent) (degrees) (percent)
180-215 4 276-295 20
216-235 6 296-315 12
236-255 30 316-335 6
256-275 21 336-360 1
heavy storms in Illinois and the Midwest. Other studies havesupported the results shown in table 22 (Huff and Vogel, 1976;Vogel and Huff, 1978).
Heavy rainstorms were found to be oriented mostfrequently from west-southwest to east-northeast throughwest to east or west-northwest to east-southeast (table 22).The median orientation of the 260 storms used in derivingtable 22 was 265° (nearly west to east). In general, it has beenfound that the orientations of very heavy storms tend to benearly west to east. Heavy, but less severe storms, are usuallyoriented west-southwest to east-northeast or west-northwestto east-southeast. Moderately heavy storms, especially thoseof short duration (1 to 3 hours), are frequently oriented west-southwest to east-northeast or southwest to northeast.
Storm MovementIn the Midwest, heavy rainstorms are usually produced
by one or more squall lines or squall areas traversinga basin or other area of interest. Each system (squall line orsquall area) consists of a number of individual convec-tive entities, usually thunderstorms, and these entities havea motion that is strongly related to the wind field in whichthey are embedded. These entities are often referred to asraincells. Network studies of the motion of heavy rain-cells (Huff, 1975) have provided the frequency distribution ofcell movements shown in table 23. The most fre-quent raincell movements are from west-southwest throughwest to west-northwest (240-299°), which accounts for42 percent of the total number analyzed in the Huff study.Of the total, 84 percent exhibited motion with a west-erly component.
Table 23. Frequency Distributionof Heavy Raincell Movements
Azimuth Storms Azimuth Storms(degrees) (percent) (degrees) (percent)
180-209 6 O-29 4
210-239 16 30-59 2
240-269 22 60-89 2
270-299 20 90-119 2
300-329 13 120-149 2
330-359 7 150-179 4
37
8. INDEPENDENCE OF EXTREME RAINFALL EVENTS
One of the problems involved in the development ofrainfall frequency distributions is the independence (or lackthereof) of the observations. This is pertinent to selectingthe method of analysis and grouping of the data in theanalytical procedures.
One method of evaluating the magnitude of this prob-lem is to examine the time distribution of the events incorpo-rated into the frequency distributions for storm periods ofvarying duration. A pilot study was made using Indiana datafor 1-, 2-, and 3-day storm periods to calculate 24-hour to 72-hour frequency relations. A total of 41 stations were used inderiving the Indiana relations.
First, the maximum recorded 1-day and 2-day amountsfor each station were examined to determine whether bothoccurred in the same storm system. Among the 41 stations, 22(54 percent) recorded both their 1-say and 2-day maxima inthe same storm systems. Thus, 54 percent of the time the 1-dayand 2-day events were not independent of each other, at leastfrom a meteorological standpoint.
Next, the same type of examination was performed on2-day and 3-day events. Results showed that 78 percent or 32stations had their maximum amounts on days when both 2-dayand 3-day records were established. Further examinationshowed that in 44 percent or 18 cases, the station maxima forall three storm periods (l-, 2-, and 3-day) occurred in singlestorm systems.
The conclusion suggested by this pilot study is thatstorm events that produce the data for deriving heavy rainfallfrequency relations cannot be assumed to represent randomoccurrences with respect to storm periods of less than 72hours. Unfortunately, these are the storm events of mostconcern to hydrologists involved in the design and operationof systems for the control of flood waters.
Next, comparisons were made between the top tenranked storms for l-day and2-day events. Among the 25 long-term stations having records of 58 to 86 years, an average of60 percent of the storm systems producing the ten largestl-day amounts also resulted in amounts ranked among the tenheaviest rain events for 2-day periods. The top ten stormevents for these long-term stations exert a strong control overrainfall amounts derived for recurrence intervals of 10 yearsor longer. The median was also 60 percent, and the rangevaried from 40 to 80 percent (4 to 8 cases) among the25 stations.
A similar analysis was made for the 16 short-termstations having records for 35-40 years. The mean and medianwere both 70 percent and the range varied from 60 to 90percent at individual stations. Because of the shorter records,the top ten ranked storms exert a strong control on determiningrecurrence-interval amounts for intervals of 5 years or longer.The comparisons between the ten heaviest storms for l-dayand 2-day rain periods strongly support the results from theanalyses of maximum recorded values described previously.
38
The same comparative analysis was applied to the topten ranked storms for 2- and 3-day periods. For the long-termstations, the average and median were both 80 percent, and therange was from 60 percent (6) to 100 percent (10). For theshort-term stations, the average and median were 76 and70 percent, respectively, and the range was from 50 to 90percent. The above comparisons for 2 and 3 days are similarto those for 1 and 2 days, and also support the earlierconclusion relating to the independence of 24- to 72-hourfrequency distributions of heavy rainfall.
Examples of Outstanding StormsA determination was made of the number of occur-
rences of rainfall amounts that ranked among the ten heaviestin some of the most widespread storms in Indiana and Illinois.For this analysis, 2-day storm periods were selected, becausemany of the heaviest storms extend from late afternoon intoevening and even later. Although these are single storms, theyare split between two days at stations of the climate net-work that report once daily at approximately 1800 CentralStandard Time.
One of the most outstanding storms occurred within a24-hour period on October 5-6, 1910. Table 24 shows thestations at which a rank 1-10 amount occurred, the amount ofrainfall, and its rank position among all storms at that station.Thus, 11 stations in Indiana qualified, and the storm rankedfirst among all storms at 4 stations. Similarly, Illinois had12 stations with 1-10 ranks, and the storm ranked first amongall storms at 5 stations. For the two states combined, therewere 23 stations with rank 1-10 storms. Of these, nineexperienced storms ranked first among 2-day storm periods.Thus 23 percent of all the 102 Indiana-Illinois stations had arank 1-10 amount, and 9 percent had their heaviest 2-daystorm on record.
Table 25 shows information on another outstanding2-day event. On March 25-26, 1913, 15 Indiana stationsrecorded rank 1-10 storms. Among these, five stations re-corded their heaviest 2-day storm on record. In Illinois, ninestations recorded rank 1-10 storms, but only one was ranked#l. Thus, for both states combined, 24 percent had rank1-10 events, and 6 percent had their most severe 2-day stormon record. This particular storm was noted by the U.S.Weather Bureau (1913):
“In a period of 4 days, beginning on March 23 andending on March 27, the average rainfall over the watershedof the WestFork of the WhiteRiver was 7.81 inches, and overthe watershed of the East Fork, 8.41 inches. This extraordi-nary rainfall produced one of the greatest floods in the historyof the state.”
A third example of outstanding storms with respect toarea enveloped and storm intensity is summarized inTable 26.This storm occurred in a 2-day period on August 14-16, 1946.It extended across central Missouri into southwestern and
39
Table 24. Distribution of Rank 1 to 10 Amounts in October 5-6, 1910, Storm in Indiana and Illinois
Station Rainfall (inches) All-storm rank
INDIANA
Bloomington 7.68 1
Moore's Hill 8.23 1
Mt. Vernon 7.68 1
Scottsburg 7.86 1
Columbus 8.12 2
Richmond 5.60 2
Rushville 5.64 2
Paoli 6.32 3
Princeton 6.33 4
Washington 5.15 5
Markland Dam 5.55 7
ILLINOIS
Cairo 9.24 1
Carbondale 8.67 1
Harrisburg 10.71 1
New Brunswick 10.72 1
Anna 9.70 1
DuQuoin 6.80 2
McLeansboro 6.42 3
Palestine 5.40 4
Fairfield 5.64 6
Flora 5.90 6
Mt. Carmel 5.16 9
Olney 5.09 10
40
Table 25. Distribution of Rank 1 to 10 Amounts in March 25-26, 1913, Storm in Indiana and Illinois
Station Amount (inches) All-storm rank
INDIANA
Columbus 8.65 1
Rushville 7.84 1
Richmond 9.47 1
Farmland 7.39 1
Washington 7.91 1
Bloomington 7.68 2
Berne 4.90 2
Marion 5.13 3
Princeton 6.37 3
Whitestown 6.07 3
Markland Dam 5.65 4
Paoli 6.25 4
Scottsburg 6.10 5
Moore's Hill 4.88 5
Rockville 4.72 8
ILLINOIS
Mt. Carmel 7.70 1
Fairfield 8.35 3
DuQuoin 6.12 4
Olney 5.59 4
Mt. Vernon 5.40 6
McLeansboro 5.95 8
Flora 5.68 8
Palestine 5.01 8
Paris 4.79 8
Table 26. Distribution of Rank 1 to 10 Amounts in August 14-16, 1946, Stormin Missouri and Illinois
Station
MISSOURI
St. Louis
Warsaw
St. Charles
Ellsberry
Salem
Boliver
Clinton
Lebanon
Rolla
Warrensburg
ILLINOIS
Belleville
Mt. Vernon
Greenville
White Hall
Pana
New Burnside
11.71 1
8.70 1
8.48 1
6.98 2
6.62 2
6.85 3
6.72 3
6.99 3
5.25 8
5.21 10
13.41 1
10.43 1
7.44 1
5.15 8
5.04 8
9
southern Illinois. As indicated in the table, ten Missouri andsix Illinois stations recorded rank 1-10 storms and six of theseranked #1 among 2-day storms on record.
Figure 20 further illustrates the areal extent and inten-sity of the storm of October 5-6, 1910. At each station, rainfallamounts (inches) and the rank of the storm in the station’shistory are shown. The northern boundary of the intenserainfall also is indicated by the dashed line. Only one reportingstation in Illinois and none in Indiana south of the dashed linefailed to report a rank 1-10 amount. It is likely that this stormalso extended into Kentucky and southeastern Missouri, butrecords for these states were not available for 1910. This wasundoubtedly one of the most severe rainstorms ever experi-enced in the nine-state area covered by this report.
Figure 21 shows the extent and intensity of the storm ofMarch 25-26, 1913, and is similar in presentation to figure 20.This storm overlapped the area incorporated in the October1910 storm. Rank 1-10 amounts were experienced at tenIndiana and seven Illinois stations in both storms. Only onereporting station within the dashed-line outline encompassing
Amount (inches) All-storm rank
most of central and northern Indiana and southeastern Illinoisdid not record a rank 1-10 storm event. This is anotherillustration of the effect imposed on point rainfall frequencyrelations by a few very extreme rainfall events.
Additional AnalysesIn view of the widespread nature of the three storms just
discussed, further analyses were undertaken to ascertain theimportance of such storm events in establishing the character-istics of rainfall frequency curves for 10-year to 100-yearrecurrence intervals. Data for Ohio, Indiana, Illinois, andMissouri were used to obtain an adequate sample of midwesternconditions. The storm data included 2-day amounts during the1901-1987 period except for Missouri where records prior to1912 were not available for all of the data set.
Two data stratifications included individual storms thatproduced five or more and ten or more qualifying amountsamong the rank 1-10 values. The analyses were made sepa-rately for each state. Results are briefly summarized in
41
Figure 20. Areal extent and magnitude of storm of October 5-6, 1910
42
Figure 21. Areal extent and magnitude of storm of March 25-26, 1913
4 3
table 27, and provide further evidence of the relatively strongdependency of 10-year to 100-year recurrence interval valueson a small portion of the heavy storm events used in establish-ing the frequency curves of point or areal mean rainfall.
In table 27, the first two columns show the total numberof observational stations used in each state and the averageprecipitation gage density (mi²/gage). Gage density willinfluence the number of rank l-10 events observed in heavystorm systems. However, except for Missouri, the gage den-sity differences among the four states are relatively small.
Following the first two columns, the number of storms(NS), the number of rank 1-10 qualifiers in these storms (NQ),and the percentage of the total number of rank l-10 values(Q%) are shown for each of the two data stratifications. Thetotal number of qualifiers is the number of stations multipliedby 10.
Table 27 indicates that the percentage of total qualifiersaccounted for by storms producing five or more qualifyingamounts varied from a high of 34 percent in Indiana to a lowof 25 percent in Ohio. The four-state average is 29 percent.The total number of qualifying storms (71) ranged from 24 in
Illinois to 12 in Ohio. The summary for those storms having10 or more qualifying amounts shows that these accounted for10 to 16 percent of the total qualifiers, and these came fromonly three to six storm events among the states.
SummaryResults of this limited study indicate that the frequency
distributions derived for 24- to 72-hour durations cannotbe assumed to be independent. Frequently, qualifyingamounts involve storm systems that dictate all three dura-tions, especially among storms that determine the 10-yearand longer recurrence-interval values. Examination ofthe heaviest IL-day storm events in Indiana and Illinoisshowed that the frequency distributions in the southern partsof these states were strongly influenced by two storms.Each of these storms produced amounts that ranked amongthe ten heaviest on record at over 20 percent of the 102reporting stations in the two states. In one storm, 9 percentof all stations received their heaviest IL-day rainfall on record,and in the other, 6 percent of all stations recordedtheir heaviest amount.
Table 27. 2-Day Storms Producing 5 or More and 10 Or More Rank 1-10 Events
≥ 5 Qualifiers ≥ 10 Qualifiers
N G NS NQ Q(%) NS NQ Q(%)
Ohio 41 1000 12 101 25 3 46 11
Indiana 41 880 18 134 34 5 64 16
Illinois 61 915 24 192 31 6 69 11
Missouri 45 1530 17 120 27 4 46 10
Notes:N = number of stationsG = gage density (mi²/gage)NS = number of qualifying stormsNQ = total number of observations in qualifying stormsQ(%) = percent of all qualifiers accounted for by NQ
4 4
9. VARIABILITY WITHIN CLIMATIC SECTIONS
Frequency relations for climatic sections and individualpoints are presented in chapter 3 and part 2. The sectionalrelations provide estimates of the expected mean rainfall forvarious recurrence intervals and rain periods in areas ofsimilar precipitation climate with respect to heavy rainfalloccurrences. Naturalvariability, however, will produce varia-tions for any given recurrence interval and storm period. Thisvariability may be substantial even when long periods ofrecord are used to develop frequency relations. Thus a mea-sure of this variability is presented here for those who requiresuch information.
The method employed involved comparing the varia-tions in rainfall amounts between the frequency distributionsderived for individual stations within a given climatic sectionand those indicated by the sectional mean distributions. Thevariability obtained by this methodresults primarily from ran-dom sampling variations due to the spatial distribution ofheavy rainstorms in a particular climatic section during thesampling period. Variability due to other causes, such asobservational and processing errors, has been minimized byusing the individual frequency distributions, rather than theraw data observations, to measure the dispersion around thesectional mean frequency distributions.
The effects of “outliers” and “inliers”, which arenonrepresentative of the expected rainfall for a given recur-rence interval and storm duration, are also minimized but notcompletely eliminated by the methods used in our nine-statestudy. “Outliers” and “inliers” are rainfall amounts that aregreater than or less than, respectively, any value expected tooccur normally within the period of record undergoing analy-sis. For example, the 200-year storm event must occur in someyear, and at some of the observational points this could haveoccurred during our observation period.
Table 28 shows the coefficient of variation, the standarddeviation divided by the mean (expressed as a percentage), foreach state for 24-hour to 10-day durations and 2-year to100-year recurrence intervals. For a normal distribution, 68percent of the observations are within one standard deviationof the mean, 95 percent are within two standard deviations,and 99 percent are within three standard deviations. Thecoefficient of variation is a measure of how well the individualstation values fit the sectional mean values. For example, ifthe coefficient of variation is 4 percent, then 68 percent (onestandard deviation) of the individual station values areexpected be within 4 percent of the mean value. Largercoefficients of variation indicate wider scatter of stationvalues above and below the sectional mean values. In prac-tice, one may construct the 95 percent confidence band (twostandard deviations) around the mean value. To do this,
multiply the coefficient of variation by 2 to get two standarddeviations (95 percent). So the 4 percent mentioned abovenow becomes 8 percent. One can then state that there is 95percent confidence that any station value in that section willfall within 8 percent of the mean value.
Initial analyses of the individual climate sections showedthat the coefficient of variation could be summarized on astate-by-state basis by averaging all climate section coeffi-cients of variation for each state. This is advantageous sinceindividual climate sections usually contained a small numberof stations, which could lead to unreliable estimates of thecoefficient of variation.
There are three general features of the coefficient ofvariation found in table 28. It tends to increase with the longerrecurrence intervals. This is due to the fact that the uncer-tainty increases because of sampling inadequacies at thelonger recurrence intervals. It also tends to decrease at longerstorm durations. This is probably because longer durationvalues are associated with large-scale precipitation events,whereas the 24- and 48-hour values are more closely relatedto small-scale, convective activity. Comparing the statesas awhole, one sees that at long recurrence intervals in Michigan,Minnesota, and Wisconsin, the coefficient of variation isgenerally higher than in the other states. The authors specu-late that this may be related to the relatively short convectiveseason in these states limiting the number of stations exposedto the large rain-producing events in a given time period.
Use of the percentages in table 28 to compute thedispersion of point rainfall values about any sectional meanfrequency distribution is illustrated in the following example.To determine the maximum positive and negative departuresthat will include 9.5 percent of the occurrences for a 50-year,24-hour storm in northwestern Illinois, refer to the meanfrequency distribution for 24-hour storms in northwesternIllinois (table 1 in part 2) or 6.53 inches.
Table 28 shows a coefficient of variation of 5 percentfor a 50-year, 24-hour storm in Illinois. Multiply 5 percent by2 to obtain the value encompassing 95 percent of the futurepoint rainfall frequency distributions for northwestern Illi-nois. Then multiply this value (10 percent) by 6.53 inches toobtain the rainfall amount to be added or subtracted from the6.53 inches to obtain the 95-percent confidence band. Thiscalculation shows that 95 percent of the point rainfall esti-mates of the 50-year, 24-hour storm are expected to fallbetween 5.88 inches and 7.18 inches. The sectional fre-quency distributions (tables in part 2) and table 28 can be usedto derive tables and curves for any climatic section and anystorm duration to obtain a measure of that section’s expectednatural variability during a particular time period (5 years, 10years, etc.).
45
46
Table 28. Dispersion of Point Rainfall Frequency Distributionsabout Section Mean Distributions
for Various Recurrence Intervals and Rain Durations
Coefficient of variation(percent)
Duration
2-Year
5-Year
10-Year
25-Year
50-Year
100-Year
ILLINOIS24-Hour 3 4 4 5 5 748-Hour 3 4 4 4 5 672-Hour 3 4 4 4 5 6 5-Day 4 4 4 4 5 610-Day 3 4 4 4 5 6
INDIANA24-Hour 5 3 4 5 7 948 -Hour 4 4 4 6 7 972-Hour 4 3 4 6 7 8 5-Day 4 4 4 5 5 610-Day 4 4 5 5 5 6
IOWA24-Hour 4 4 5 7 8 948-Hour 4 4 5 7 8 972-Hour 3 4 5 6 7 8 5-Day 4 3 4 5 5 710-Day 3 4 4 4 4 5
KENTUCKY24-Hour 6 5 7 7 8 948 -Hour 6 5 6 7 8 972-Hour 5 5 6 7 7 8 5-Day 6 5 6 7 7 710-Day 6 5 6 5 5 5
MICHIGAN24-Hour 4 4 5 6 8 1048-Hour 4 4 5 6 8 972-Hour 3 4 5 6 7 9 5-Day 4 5 5 6 7 810-Day 4 4 5 6 7 8
MINNESOTA24-Hour 4 5 6 9 10 1248-Hour 4 5 6 8 10 1372-Hour 4 5 6 8 8 11 5-Day 4 4 5 6 8 910-Day 5 4 5 6 6 7
MISSOURI24-Hour 4 5 6 7 8 848-Hour 4 5 5 6 7 772-Hour 4 5 5 6 6 7 5-Day 4 4 5 5 5 510-Day 4 4 4 4 5 6
47
Table 28. Concluded
Duration2-
Year5-
Year10-
Year25-
Year50-
Year100-Year
OHIO
24-Hour 4 5 6 6 7 748-Hour 4 4 5 5 6 772-Hour 4 4 5 6 6 7 5-Day 5 5 5 5 6 710-Day 4 5 5 6 7 8
WISCONSIN24-Hour 4 4 5 6 8 1048-Hour 4 4 5 6 8 1072-Hour 4 4 5 6 8 10 5-Day 4 4 5 6 8 910-Day 3 3 3 4 5 7
10. GENERAL SUMMARY AND CONCLUSIONS
The basic philosophy applied in our nine-state study isthat a combination of appropriate statistical techniques, guidedby available meteorological and climatological knowledge ofheavy rainfall events, provides the best approach to develop-ing reliable frequency distributions. It was recognized that thenatural laws controlling the atmospheric processes are notgoverned by any specific statistical distribution. Within thelimits of the data sampled, however, the application ofappropriate statistical analysis provides a means of optimiz-ing the information contained in that data.
Initially, a very detailed study of Illinois frequencyrelations was made. Methods and techniques developed in thisstudy were then applied in the other eight midwestem states.Illinois is located near the center of this nine-state area, andthere are no major changes in the general precipitation climatewithin this region.
Data and Analytical ApproachThe study relied primarily upon data for 275 daily
reporting stations within the NWS cooperative network. Allof these stations had records exceeding 50 years. These datawere supplemented by 134 cooperative stations with shorterrecords, by first-order station data, and by recording raingagedata where available. Because the cooperative network pro-vides only daily amounts of precipitation, well-establishedempirical factors were used to convert calendar-day rainfallto maximum 24-, 48-, and 72-hour amounts. Recurrence-interval amounts for rain periods of less than 24 hours wereobtained from average ratios of x-hour/24-hour rainfall. Theseratios were determined primarily from recording raingagedata for 1948-1983 at 34 Illinois stations and 21 stations inadjoining states. Frequency relations for time periods shorterthan 12 months were calculated from ratios relating x-month/24-month rainfall for various recurrence intervals.
For each station, the data were used to determine theannual maxima time series. Station frequency curves werethen derived from the annual series values. For this report,however, the annual maxima values were converted to partialduration values. The annual maxima series is more adaptableto statistical testing, but the partial duration values are pre-ferred by most users, especially engineers involved in thedesign and operation of water control structures.
Statistical MethodsAs part of our nine-state research, an evaluation was
made of various statistical methods and techniques con-sidered to have potential for use in deriving the frequencydistributions of heavy rainstorms. Major emphasis was placedon the applicability of (1) the L-moments method, which hasreceived considerable attention in recent years; (2) the maxi-mum likelihood methods; and (3) the Huff-Angel methodused in the nine-state study. Except in a small percentage of
48
the cases, the methods provided results that were not sig-nificantly different from either a statistical or meteorologicalstandpoint, considering the inherent variability (real andhuman-induced) in the data samples. In general, theHuff-Angel estimates lie between those of the other twomethods. From selected isohyetal maps comparing theL-moments and Huff-Angel distributions, it was concludedthat the Huff-Angel spatial patterns conformed somewhatbetter with available climatological knowledge on thedistribution of heavy storm rainfall in the Midwest. Thelargest differences occurred most frequently with the100-year estimates, which represent an extension beyond thelimits of all the data samples. Unfortunately, there is noreliable method of determining which estimate is “best” atpredicting the most severe events.
Frequency Distributionof Heavy Rainfall Events
In our nine-state study, two methods of data analysis andpresentation of results were used. For the first method, pointrainfall frequencies were developed and presented in the formof isohyetal maps for various recurrence intervals and stormdurations. This is the method most commonly used by pastinvestigators. For the second method, areal mean rainfallfrequency relations were developed in each state for regionsof approximately homogeneous heavy rainfall climate. Forboth methods, frequency relations were developed for recur-rence intervals ranging from 2 months to 100 years, and forrain periods varying from 5 minutes to 10 days. This widerange of frequency values was considered necessary to meetthe needs of all potential users.
Point Rainfall Frequency DistributionsIsohyetal maps derived from individual station fre-
quency relations were used to portray the spatial distributionof point rainfall for selected rain periods of 1 hour to 10 days,and recurrence intervals ranging from 2 to 100 years. Otherrain durations and recurrence intervals can be calculated bytransformation factors provided earlier in this report (tables 3and 4). The isohyetal presentation is susceptible to consider-able subjectivity and to natural and human-induced samplingerrors undetected by statistical analyses. The method is usefuland familiar to most users, however, and allows forincorporation of small-scale spatial differences resultingfrom localized influences if the sampling density is adequate.
Area1 Mean Rainfall Frequency DistributionsIn the Midwest, consideration of available climate
information on the distribution of heavy rainstorms, alongwith climatological-meteorological knowledge of storm sys-tem characteristics, indicated that the well-established NWSclimate divisions could be used to divide the states intoapproximately homogeneous climate regions with respect to
the frequency and intensity of extreme rainfall events. Foreach division, average frequency distributions were thendeveloped using all stations within the division and thosein neighboring divisions near its boundaries. The fore-going technique does not eliminate potential samplingerrors in the data samples, but it does moderate their effects.Unless the divisions are properly selected, however, theaveraging techniques may mask actual small-scale effects.This problem would be more acute in regions incorporatingmajor changes in topography, such as the Appalachian andRocky Mountain regions.
Time Distributions of Rainfallin Heavy Storms
Modem runoff models for urban and small-basin de-signs of water-control structures require definition of the timedistribution characteristics within heavy rainstorms. Conse-quently, statistical time distributions developed for varioustypes of storm systems in an Illinois study have been incorpo-rated into this report. Although based on dense raingagenetwork data in Illinois, the relationships should be applicablein the nine-state region and other areas of similar precipitationclimate. These time distribution models can be used inconjunction with the frequency distributions presented in thisreport to accommodate hydrological needs.
Seasonal Distribution of Heavy RainfallThe seasonal distribution of heavy rainstorms is
pertinent in hydrology, agriculture, and other fields. In ournine-state study, available resources prohibited an extensiveevaluation of seasonal rainfall frequencies. Three studieswere pursued on a limited basis, however, and their results areincluded in this report to partially meet existing needs forseasonal information. These studies involved (1) the distribu-tion of total precipitation in each of the four seasons (spring,summer, fall, and winter), (2) the seasonal distribution ofheavy rainstorms, and (3) general characteristics and differ-ences in seasonal frequency relations throughout the nine-state region. Results indicate that in the northern parts of theregion, heavy rainstorms occur most often in the summer,followed by spring and fall, and are practically nonexistent inwinter. In the southern parts of Missouri, Indiana, and Ohio,however, the heaviest amounts in long-duration storms (5 to10 days) are most likely to occur in the cold season frommid-fall to early spring.
Temporal Fluctuations in FrequencyDistribution of Heavy Rainstorms
Using the available data sample for 1901-1987, aninvestigation was made to determine whether climate trendsor long-term fluctuations were indicated. For this purpose, thedata were divided to provide two 40-year samples. Amountsfor 2-, 5, and 10-year recurrence intervals were analyzed. The87-year sample was not adequate to examine longer periods.Comparisons were then made between the various periods. Ingeneral, the results indicated an area of increasing frequencyand/or intensity of heavy storms along an axis extending fromMissouri north east ward through Illinois to southern Michiganand northern Ohio. The increases appear to be greater thanexpected from climate variability and sufficiently large tohave an impact on water-control structural designs and otheraspects of applied climatology.
Other StudiesLimited information has been presented that relates
to the various spatial characteristics of heavy rainstorms inthe Midwest. These have been abstracted from Illinois studiesand include the relationship between point and arealmean rainfall frequency, storm shape, storm orientation, andstorm movement.
Another limited study was concerned with the indepen-dence of extreme rainfall events. One of the numerous prob-lems involved in the development of rainfall frequencyrelations is the independence of the observations. Resultsindicated that the 1-day to 3-day frequency distributionsfrequently involve storm systems that dictate all three events.This is especially evident in those storms that produce the longrecurrence-interval values. For example, examination of theheaviest 2-day storm events in Indiana and Illinois showedthat the frequency distributions in the southern parts of thesestates were strongly influenced by two storms. In the storm ofOctober 5-6, 1910, 23 percent of all the long-period reportingstations in the two states had 2-day amounts that rankedamong the ten heaviest on record, and 9 percent recorded theirheaviest storm of the 1901-1987 period. In the storm of March25-26, 1913, 24 percent of the stations in the two statesreported amounts that rank among the first ten on record, and6 percent had their heaviest storm ever observed.
49
REFERENCES
Changnon, S.A., Jr. 1983. “Trends in Floods and RelatedClimate Conditions in Illinois.” Climatic Change 5:341-363.
Changnon, S.A., Jr. 1984. Climate Fluctuations in Illinois,1901-1984. Illinois State Water Survey Bulletin 68. 73 p.
Changnon, S.A., Jr. 1985. “Climatic Fluctuations and Impacts.”Bulletin of the American Meteorological Society 66:142-151.
Changnon, S.A., and J.M. Changnon. 1989. “DevelopingRainfall Insurance Rates for the Contiguous United States.”Journal of Applied Meteorology 28:1185-1196.
Changnon, S.A., Jr., F.A. Huff, P.T. Schickedanz, and J.L.Vogel. 1977. Summary of METROMEX, Volume 1: WeatherAnomalies and Impacts. Illinois State Water Survey Bulletin62, 260 p.
Changnon, S.A., Jr., and J.L. Vogel. 1981. “Hydroclima-tological Characteristics of Isolated Severe Rainstorms.”Water Resources Research 17(6):1694-1700.
Chow, Ven Te. 1954. “The Log-Probability Law and itsEngineering Applications.” Proceedings American Societyof Civil Engineering, 80, Separate 536.
Farago, T., and R.W. Katz. 1990. Extremes and DesignValues in Climatology. World Meteorological Organization,TD-No. 386, Geneva, Switzerland.
Gumbel, E.J. 1941. “The Return Period of Flood Flows.” TheAnnals of Mathematical Statistics 12(2):163-190.
Gumbel, E.J. 1956. “Methodes Graphiques pourl’analyse desDebits de Crue (avec une Intervention de J. Bemier).” Extraitde Houille Blanche, Numero 5, Rue Paul-Verlaine, Grenoble,France.
Hershfield, D.M. 1961. Rainfall Frequency Atlas of theUnited States. U.S. Department of Commerce, Weather BureauTechnical Paper 40, 115 p.
Hosking, J.R.M. 1990. “L-Moments: Analysis and Estimationof Distributions Using Linear Combinations of OrderStatistics.” Journal of the Royal Statistical Society, B ,52(1):105-124.
Hosking, J.R.M. 1991. Fortran Routines for Using theMethod of L-Moments, Version 2. IBM Research ReportRC17097, IBM Research Division, T.J. Watson Center,Yorktown Heights, NY.
Hosking, J.R.M., and JR. Wallis 1991. Some Statistics Usefulin Regional Frequency Analysis. IBM Research ReportRC17096, IBM Research Division, T.J. Watson ResearchCenter, Yorktown Heights, NY.
Huff, F.A. 1967. “Time Distribution of Rainfall in HeavyStorms.” Water Resources Research 11:889-896.
50
Huff, F.A. 1970. Rainfall Evaluation Studies. Final Report forNational Science Foundation, Part I, Grant GA-1360,53 p.
Huff, F.A. 1975. “Urban Effects on the Distribution ofHeavy Convective Rainfall.” Water Resources Research11:889-896.
Huff, F.A. 1986. “Urban Hydrometeorology Review.” Bulletinof the American Meteorological Society 67(6):703-712.
Huff, F.A. 1990. Time Distributions of Heavy Rainstorms inIllinois. Illinois State Water Survey Circular 173, 18 p.
Huff, F.A., and J.R. Angel. 1989. Frequency Distributionsand Hydroclimatic Characteristics of Heavy Rainstormsin Illinois. Illinois State Water Survey Bulletin 70, 177 p.
Huff, F.A., and J.R. Angel. 1990. “Fluctuations in the Fre-quency Distributions of Heavy Rainstorms in the Midwest.”Preprint, American Meteorological Society Symposium onGlobal Change Systems, Anaheim, CA.
Huff, F.A., and S.A. Changnon, Jr. 1973. “PrecipitationModification by Major Urban Areas.” Bulletin of the AmericanMeteorological Society 54:1220-1232.
Huff, F.A., and S.A. Changnon. 1987. “Temporal Changes inDesign Rainfall Frequencies in Illinois.” Climatic Change10:195-200.
Huff, F.A., S.A. Changnon, and D.M.A. Jones. 1975.“Precipitation Increases in the Low Hills of Southern Illinois:Part 1, Climate and Network Studies.” Monthly Weather Re-view 103(9):823-829.
Huff, F.A., and J.C. Neill. 1959a. Frequency Relationsfor Storm Rainfall in Illinois. Illinois State Water SurveyBulletin 46. 65 p.
Huff, F.A., and J.C. Neill. 1959b. “Comparison of SeveralMethods for Rainfall Frequency Analysis.” Journal ofGeophysical Research 4:541-547.
Huff, F.A., and R.G. Semonin. 1960. An Investigation ofFlood-Producing Storms in Illinois. American Meteorolog-ical Society Monograph, No. 4, pp.50-55.
Huff, F.A., and J.L. Vogel. 1976. Hydrometeorology of HeavyRainstorms in Chicago and Northeastern Illinois. Illinois StateWater Survey Report of Investigation 82, 66 p.
Jenkinson, A.F. 1955. “Frequency Distribution of MaximumValues.” Quarterly Journal of the Royal Meteorological So-ciety 81:158-171.
Kite, G.W. 1977. Frequency and Risk Analysis in Hydrology.Water Resources Publications, Fort Collins, CO, 224 p.
Miller, J.F., R.H. Frederick, and R.J. Tracey. 1973.Precipitation-Frequency Atlas of the Western United States.
NOAA Atlas 2, National Weather Service, SilverSprings, MD.
Reich, B.M. 1972. “Log-Pearson Type 3 and Gumbel Analysisof Floods.” Second International Symposium in Hydrology,Fort Collins, CO, pp.290-303.
Sevruk, B., and H. Geiger. 1980. Selection of DistributionTypes for Extremes of Precipitation. World MeteorologicalOrganization, Gperational Hydrology Report No. 15, Geneva,Switzerland.
Soil Conservation Service. 1968. Hydrology, Supplement Aof Section 4, Engineering Handbook. U.S. Department ofAgriculture, Washington, DC.
Sorrell, R.C., and D.A. Hamilton. 1990. Rainfall Frequencyfor Michigan, 24-Hour Duration with Return Periods from 2to 100 Years. Draft. Michigan Department of NaturalResources, Lansing, MI, 24 p.
Stout, G.E., and F.A. Huff. 1962. “Studies of Severe Rain-storms in Illinois.” Journal of Hydraulics Division, AmericanSociety of Civil Engineers 88(HY4):129-146.
U.S. Weather Bureau. 1913. Climatological Data, AnnualSummary. U.S. Department of Commerce, Weather Bureau,Washington, DC, p.3.
U.S. Weather Bureau. 1953. Rainfall Intensities for LocalDrainage Design in the United States. Technical Paper No.24, Part I, U.S. Department of Commerce, Weather Bureau,Washington, DC, 19 p.
Vogel, J.L. 1988. An Examination of Chicago PrecipitationPatterns for Water Year 1984. Illinois State Water SurveyContract Report 449, 44 p.
Vogel, J.L., and F.A. Huff 1978. “Relation Between the St.Louis Urban Precipitation Anomaly and Synoptic WeatherFactors.” Journal of Applied Meteorology 17(8):1141-1152.
Wallis, J.R. 1989. Regional Frequency Studies UsingL-Moments . Research Report RC 14597 (#65218),IBM Research Division, T.J. Watson Center, YorktownHeights, NY.
Yarnell, D.L. 1935. Rainfall Intensity-Frequency Data. U.S.Department of Agriculture, Miscellaneous Publication No.204, Washington, DC, 35 p.
5 1
Part 2. Spatial Distribution Maps and Sectional Mean FrequencyDistribution Tables
(The data for the sectional mean frequency tables are available on disk from the Midwestern Climate Centerat the Illinois State Water Survey. Please call (217)244-8226 for further information.)
The user should consult the introduction and chapter 3 in part 1 before using the maps and tables in part 2 tounderstand their strengths and weaknesses.
53
2-yr. 1 hr.
Figure 1. Spatial distribution of 1 -hour rainfall (inches)
54
5yr. 1 hr.
Figure 1. Continued
55
10 yr. 1 hr.
Figure 1. Continued
56
25-yr. 1 hr.
Figure 1. Continued
57
50-yr. 1 hr.
Figure 1. Continued
58
l00-yr. 1 hr.
Figure 1. Concluded
59
2-yr. 2 hr.
Figure 2. Spatial distribution of 2-hour rainfall (inches)
60
5-yr. 2 hr.
Figure 2. Continued
61
10 yr. 2 hr.
Figure 2. Continued
62
25-yr. 2 hr.
Figure 2. Continued
63
50-yr. 2 hr.
Figure 2. Continued
64
Figure 2. Concluded
100-yr. 2 hr.
65
2-yr. 3-hr.
Figure 3. Spatial distribution of 3-hour rainfall (inches)
66
Figure 3. Continued
5-yr. 3-hr.
67
10-yr. 3-hr.
Figure 3. Continued
68
25-Yr. 3-hr.
Figure 3. Continued
69
50-yr. 3-hr.
Figure 3. Continued
70
Figure 3. Concluded
100-yr. 3-hr.
71
2-yr. 6-hr.
Figure 4. Spatial distribution of 6-hour rainfall (inches)
72
5-yr. 6-hr.
Figure 4. Continued
73
10-yr. 6-hr.
Figure 4. Continued
74
Figure 4. Continued
25-yr. 6-hr.
75
50-yr. 6-hr.
Figure 4. Continued
76
100-yr. 6-hr.
Figure 4. Concluded
77
2-yr. 12-hr.
Figure 5. Spatial distribution of 12-hour rainfall (inches)
78
5-yr. 12-hr.
Figure 5. Continued
79
Figure 5. Continued
80
10-yr.12-hr.
Figure 5. Continued
25-yr. 12-hr.
81
50-yr. 12-hr.
Figure 5. Continued
82
l00-yr. 12-hr.
Figure 5. Concluded
83
2-yr. 24-hr.
Figure 6. Spatial distribution of 24-hour rainfall (inches)
84
5-yr. 24.hr.
Figure 6. Continued
85
10-yr. 24-hr.
Figure 6. Continued
86
25-yr. 24-hr.
Figure 6. Continued
87
50-yr. 24-hr.
Figure 6. Continued
88
100-yr. 24-hr.
Figure 6. Concluded
89
90
Figure 7. Spatial distribution of 48-hour rainfall (inches)
2-yr. 48 hr.
5-yr. 48 hr.
Figure 7. Continued
91
10 yr. 48 hr.
92
Figure 7. Continued
25-yr. 48 hr.
Figure 7. Continued
93
Figure 7. Continued
50-yr. 48 hr.
9 4
100-yr. 48 hr.
95
Figure 7. Concluded
2-yr. 72 hr.
Figure 8. Spatial distribution of 72-hour rainfall (inches)
96
Figure 8. Continued
5-yr. 72 hr.
97
10yr.72hr.
Figure 8. Continued
98
25-yr. 72 hr.
99
Figure 8. Continued
50-yr. 72 hr.
Figure 8. Continued
100
Figure 8. Concluded
l00-yr. 72 hr.
101
2-yr. 5 day
Figure 9. Spatial distribution of 5-day rainfall (inches)
102
5-yr. 5 day
Figure 9. Continued
103
10-yr. 5 day
104
Figure 9. Continued
25-yr. 5 day
Figure 9. Continued
105
50-yr. 5 day
Figure 9. Continued
106
l00-yr. 5 day
Figure 9. Concluded
107
2-yr. 10 day
Figure 10. Spatial distribution of 10-day rainfall (inches)
108
5-yr. 10 day
Figure 10. Continued
109
10-yr. 10 day
Figure 10. Continued
110
25-yr. 10 day
Figure 10. Continued
111
50-yr. 10 day
Figure 10. Continued
112
Figure 10. Concluded
l00-yr. 10 day
113
114
Table 1. Sectional Mean Frequency Distributions for Storm Periods of 5 Minutes to 10 Daysand Recurrence Intervals of 2 Months to 100 Years in Illinois
Sectional code (see figure 1 on page 4)
01 - Northwest 06 - West Southwest02 - Northeast 07 - East Southeast03 - West 08 - Southwest
04 - Central 09 - Southeast05 - East 10 – South
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
01 10-day 2.14 2.60 2.97 3.50 4.02 4.37 5.23 6.30 7.14 8.39 9.64 11.0901 5-day 1.76 2.12 2.38 2.76 3.17 3.45 4.13 5.10 5.91 7.21 8.36 9.9701 72-hr 1.58 1.90 2.11 2.45 2.82 3.06 3.73 4.67 5.42 6.59 7.64 8.8701 48-hr 1.47 1.74 1.93 2.24 2.58 2.80 3.42 4.28 4.96 6.07 7.02 8.0701 24-hr 1.40 1.64 1.80 2.08 2.36 2.57 3.11 3.95 4.63 5.60 6.53 7.3601 18-hr 1.30 1.52 1.66 1.92 2.18 2.37 2.86 3.63 4.26 5.15 6.01 6.9201 12-hr 1.23 1.43 1.57 1.81 2.06 2.24 2.71 3.43 4.03 4.88 5.66 6.5101 6-hr 1.06 1.24 1.37 1.56 1.77 1.93 2.33 2.96 3.48 4.20 4.90 5.6901 3-hr 0.91 1.06 1.16 1.33 1.52 1.65 1.99 2.53 2.97 3.59 4.18 4.9001 2-hr 0.84 0.97 1.06 1.23 1.40 1.52 1.83 2.33 2.74 3.31 3.86 4.4701 1-hr 0.67 0.78 0.86 0.98 1.11 1.21 1.46 1.86 2.18 2.63 3.07 3.5101 30-min 0.52 0.61 0.68 0.77 0.87 0.95 1.15 1.46 1.71 2.07 2.42 2.7701 15-min 0.38 0.45 0.50 0.57 0.64 0.70 0.84 1.07 1.25 1.51 1.76 1.9901 10-min 0.31 0.36 0.40 0.46 0.52 0.57 0.68 0.87 1.02 1.23 1.44 1.6201 5-min 0.17 0.20 0.22 0.25 0.29 0.31 0.37 0.47 0.56 0.67 0.78 0.89
02 10-day 2.02 2.48 2.80 3.30 3.79 4.12 4.95 6.04 6.89 8.18 9.38 11.1402 5-day 1.66 1.98 2.24 2.60 2.99 3.25 3.93 4.91 5.70 6.93 8.04 9.9602 72-hr 1.53 1.83 2.02 2.34 2.70 2.93 3.55 4.44 5.18 6.32 7.41 8.7802 48-hr 1.44 1.70 1.90 2.18 2.49 2.70 3.30 4.09 4.81 5.88 6.84 8.1602 24-hr 1.38 1.61 1.76 2.03 2.31 2.51 3.04 3.80 4.47 5.51 6.46 7.5802 18-hr 1.26 1.47 1.61 1.86 2.12 2.30 2.79 3.50 4.11 5.06 5.95 6.9702 12-hr 1.20 1.40 1.53 1.77 2.01 2.18 2.64 3.31 3.89 4.79 5.62 6.5902 6-hr 1.03 1.21 1.32 1.52 1.74 1.88 2.28 2.85 3.35 4.13 4.85 5.6802 3-hr 0.88 1.02 1.13 1.30 1.47 1.60 1.94 2.43 2.86 3.53 4.14 4.8502 2-hr 0.81 0.95 1.05 1.20 1.36 1.48 1.79 2.24 2.64 3.25 3.82 4.4702 1-hr 0.65 0.76 0.84 0.96 1.09 1.18 1.43 1.79 2.10 2.59 3.04 3.5602 30-min 0.51 0.60 0.65 0.75 0.86 0.93 1.12 1.41 1.65 2.04 2.39 2.8002 15-min 0.37 0.44 0.48 0.55 0.63 0.68 0.82 1.03 1.21 1.49 1.75 2.0502 10-min 0.30 0.35 0.39 0.45 0.51 0.55 0.67 0.84 0.98 1.21 1.42 1.6702 5-min 0.17 0.19 0.21 0.24 0.28 0.30 0.36 0.46 0.54 0.66 0.78 0.91
03 10-day 2.27 2.78 3.13 3.68 4.23 4.60 5.60 6.91 7.89 9.24 10.36 11.9003 5-day 1.92 2.30 2.56 2.97 3.41 3.71 4.57 5.80 6.65 7.90 8.95 10.5003 72-hr 1.72 2.05 2.28 2.64 3.02 3.30 4.08 5.11 5.87 6.97 7.95 9.4803 48-hr 1.61 1.88 2.09 2.42 2.76 3.01 3.68 4.56 5.50 6.45 7.56 8.8003 24-hr 1.53 1.77 1.95 2.24 2.56 2.79 3.45 4.29 4.93 6.07 7.04 8.2003 18-hr 1.41 1.64 1.80 2.07 2.36 2.57 3.18 3.95 4.53 5.59 6.47 7.5503 12-hr 1.34 1.56 1.70 1.94 2.22 2.43 2.98 3.73 4.29 5.28 6.13 7.1403 6-hr 1.15 1.34 1.47 1.67 1.91 2.10 2.58 3.22 3.70 4.55 5.28 6.1503 3-hr 0.98 1.15 1.26 1.44 1.65 1.79 2.21 2.75 3.15 3.89 4.51 5.2503 2-hr 0.91 1.06 1.17 1.32 1.50 1.65 2.02 2.53 2.91 3.58 4.15 4.8403 1-hr 0.72 0.84 0.92 1.06 1.21 1.31 1.60 2.02 2.32 2.86 3.31 3.8503 30-min 0.57 0.66 0.73 0.83 0.95 1.03 1.27 1.59 1.82 2.25 2.61 3.0303 15-min 0.41 0.48 0.53 0.61 0.69 0.75 0.91 1.16 1.33 1.64 1.90 2.2103 10-min 0.34 0.39 0.43 0.49 0.56 0.61 0.74 0.94 1.08 1.33 1.55 1.8103 5-min 0.18 0.21 0.23 0.26 0.30 0.33 0.40 0.51 0.59 0.73 0.84 0.98
115
Table 1. Continued
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
04 10-day 2.10 2.58 2.92 3.43 3.93 4.29 5.12 6.27 7.10 8.19 9.10 10.1804 5-day 1.77 2.12 2.37 2.78 3.20 3.48 4.17 5.11 5.84 6.96 7.98 9.2104 72-hr 1.59 1.91 2.12 2.44 2.80 3.05 3.70 4.55 5.26 6.15 7.25 8.1604 48-hr 1.48 1.76 1.95 2.25 2.58 2.81 3.38 4.19 4.86 5.78 6.62 7.5104 24-hr 1.39 1.63 1.80 2.04 2.32 2.52 3.02 3.76 4.45 5.32 6.08 6.9204 18-hr 1.27 1.51 1.66 1.88 2.12 2.28 2.75 3.46 4.09 4.90 5.59 6.3704 12-hr 1.19 1.40 1.53 1.77 2.01 2.17 2.62 3.27 3.87 4.63 5.29 6.0204 6-hr 1.03 1.21 1.34 1.53 1.74 1.89 2.26 2.82 3.33 3.99 4.56 5.1904 3-hr 0.89 1.03 1.13 1.30 1.47 1.61 1.93 2.41 2.85 3.41 3.89 4.4304 2-hr 0.82 0.95 1.04 1.19 1.37 1.48 1.78 2.22 2.62 3.14 3.59 4.0804 1-hr 0.65 0.76 0.83 0.95 1.09 1.18 1.42 1.77 2.09 2.50 2.86 3.2504 30-min 0.52 0.60 0.66 0.75 0.86 0.93 1.12 1.39 1.64 1.97 2.25 2.5604 15-min 0.37 0.44 0.49 0.56 0.63 0.68 0.81 1.02 1.20 1.44 1.64 1.8704 10-min 0.30 0.35 0.39 0.45 0.50 0.55 0.66 0.83 0.98 1.17 1.34 1.5204 5-min 0.17 0.19 0.21 0.24 0.28 0.30 0.36 0.45 0.53 0.64 0.73 0.83
05 10-day 2.13 2.62 2.96 3.48 4.00 4.35 5.15 6.21 6.97 8.04 8.90 9.9205 5-day 1.75 2.10 2.37 2.75 3.15 3.42 4.12 4.96 5.67 6.76 7.65 8.7805 72-hr 1.61 1.93 2.16 2.48 2.85 3.10 3.71 4.57 5.20 6.17 6.97 7.8305 48-hr 1.51 1.77 1.95 2.26 2.57 2.82 3.40 4.16 4.77 5.66 6.40 7.1605 24-hr 1.36 1.58 1.75 2.00 2.27 2.47 3.01 3.71 4.26 5.04 5.83 6.6105 18-hr 1.25 1.47 1.62 1.84 2.09 2.27 2.77 3.41 3.92 4.63 5.37 6.0805 12-hr 1.18 1.38 1.53 1.74 1.98 2.15 2.62 3.23 3.71 4.38 5.08 5.7505 6-hr 1.00 1.18 1.32 1.49 1.70 1.85 2.26 2.78 3.20 3.78 4.38 4.9605 3-hr 0.87 1.02 1.12 1.28 1.46 1.58 1.93 2.37 2.73 3.22 3.74 4.2305 2-hr 0.79 0.93 1.03 1.17 1.34 1.46 1.78 2.19 2.52 2.97 3.44 3.9005 1-hr 0.64 0.74 0.81 0.93 1.07 1.16 1.41 1.74 2.00 2.39 2.74 3.1105 30-min 0.50 0.58 0.64 0.74 0.84 0.91 1.11 1.37 1.57 1.87 2.16 2.4505 15-min 0.37 0.43 0.47 0.54 0.62 0.67 0.81 1.00 1.14 1.37 1.60 1.8505 10-min 0.30 0.35 0.38 0.43 0.49 0.54 0.66 0.81 0.94 1.12 1.28 1.4605 5-min 0.17 0.19 0.21 0.24 0.28 0.30 0.36 0.44 0.51 0.61 0.70 0.79
06 10-day 2.16 2.65 2.99 3.52 4.05 4.40 5.35 6.62 7.45 8.66 9.79 11.2606 5-day 1.77 2.13 2.39 2.78 3.19 3.47 4.19 5.32 6.20 7.44 8.53 9.9306 72-hr 1.63 1.95 2.16 2.50 2.88 3.13 3.81 4.85 5.68 6.84 7.76 8.9206 48-hr 1.52 1.81 2.00 2.30 2.64 2.87 3.49 4.45 5.21 6.28 7.12 8.1906 24-hr 1.42 1.66 1.84 2.10 2.38 2.59 3.11 3.93 4.65 5.57 6.46 7.4506 18-hr 1.31 1.53 1.68 1.93 2.19 2.38 2.86 3.61 4.28 5.12 5.95 6.8506 12-hr 1.24 1.44 1.57 1.82 2.07 2.25 2.71 3.39 3.97 4.84 5.62 6.4806 6-hr 1.07 1.24 1.37 1.57 1.78 1.94 2.33 2.95 3.48 4.18 4.85 5.5906 3-hr 0.91 1.07 1.18 1.34 1.52 1.66 1.99 2.51 2.98 3.56 4.14 4.7706 2-hr 0.84 0.98 1.08 1.24 1.41 1.53 1.84 2.32 2.74 3.28 3.81 4.3906 1-hr 0.67 0.79 0.87 0.99 1.12 1.21 1.46 1.85 2.19 2.62 3.04 3.5006 30-min 0.53 0.61 0.68 0.78 0.88 0.96 1.15 1.46 1.72 2.06 2.39 2.7506 15-min 0.38 0.45 0.49 0.57 0.64 0.70 0.84 1.06 1.26 1.52 1.75 2.0106 10-min 0.31 0.36 0.40 0.46 0.52 0.57 0.68 0.87 1.02 1.22 1.42 1.6406 5-min 0.17 0.20 0.22 0.25 0.29 0.31 0.37 0.47 0.56 0.67 0.78 0.89
116
Table 1. Continued
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
07 10-day 2.30 2.80 3.16 3.70 4.27 4.64 5.58 6.80 7.61 8.66 9.70 10.87 07 5-day 1.85 2.22 2.50 2.90 3.31 3.63 4.34 5.33 6.11 7.28 8.37 9.65 07 72-hr 1.62 1.90 2.15 2.50 2.87 3.12 3.73 4.64 5.32 6.39 7.35 8.54 07 48-hr 1.52 1.78 1.98 2.30 2.64 2.87 3.42 4.26 4.88 5.84 6.75 8.00 07 24-hr 1.40 1.63 1.78 2.07 2.35 2.55 3.03 3.80 4.44 5.37 6.23 7.41 07 18-hr 1.29 1.50 1.64 1.90 2.16 2.35 2.79 3.49 4.08 4.94 5.73 6.81 07 12-hr 1.21 1.42 1.55 1.80 2.04 2.22 2.63 3.30 3.86 4.67 5.42 6.45 07 6-hr 1.06 1.23 1.37 1.55 1.74 1.87 2.27 2.85 3.33 4.03 4.67 5.56 07 3-hr 0.89 1.05 1.15 1.32 1.50 1.63 1.94 2.43 2.84 3.44 3.99 4.74 07 2-hr 0.83 0.97 1.07 1.22 1.38 1.50 1.79 2.24 2.62 3.17 3.67 4.39 07 1-hr 0.66 0.77 0.85 0.97 1.10 1.20 1.42 1.78 2.09 2.52 2.93 3.48 07 30-min 0.52 0.60 0.66 0.76 0.86 0.93 1.12 1.41 1.64 1.99 2.31 2.74 07 15-min 0.38 0.44 0.49 0.56 0.63 0.69 0.82 1.03 1.20 1.45 1.68 2.00 07 10-min 0.31 0.36 0.40 0.45 0.51 0.56 0.66 0.83 0.98 1.18 1.37 1.63 07 5-min 0.17 0.20 0.22 0.25 0.29 0.31 0.36 0.46 0.54 0.64 0.75 0.89
08 10-day 2.22 2.74 3.09 3.63 4.18 4.54 5.54 6.80 7.80 9.20 10.44 11.81 08 5-day 1.85 2.21 2.49 2.90 3.31 3.62 4.40 5.46 6.34 7.68 8.88 10.68 08 72-hr 1.67 1.97 2.20 2.54 2.93 3.22 3.94 4.92 5.74 6.97 8.12 9.55 08 48-hr 1.57 1.85 2.06 2.38 2.75 2.97 3.59 4.52 5.26 6.43 7.36 8.81 08 24-hr 1.49 1.73 1.90 2.20 2.48 2.71 3.28 4.13 4.76 6.02 7.07 8.21 08 18-hr 1.35 1.59 1.74 2.00 2.29 2.49 3.02 3.80 4.38 5.54 6.51 7.55 08 12-hr 1.28 1.50 1.64 1.88 2.15 2.35 2.86 3.60 4.14 5.24 6.15 7.14 08 6-hr 1.12 1.30 1.44 1.64 1.87 2.03 2.45 3.10 3.57 4.52 5.30 6.16 08 3-hr 0.95 1.12 1.22 1.40 1.59 1.73 2.10 2.63 3.08 3.86 4.52 5.25 08 2-hr 0.88 1.02 1.13 1.28 1.47 1.60 1.94 2.44 2.87 3.55 4.20 4.84 08 1-hr 0.70 0.81 0.89 1.02 1.15 1.26 1.54 1.93 2.27 2.84 3.32 3.86 08 30-min 0.55 0.64 0.71 0.81 0.92 1.00 1.22 1.53 1.78 2.25 2.62 3.03 08 15-min 0.40 0.47 0.52 0.59 0.67 0.73 0.89 1.12 1.29 1.63 1.91 2.22 08 10-min 0.33 0.38 0.42 0.49 0.55 0.60 0.72 0.91 1.05 1.32 1.55 1.81 08 5-min 0.18 0.21 0.23 0.26 0.30 0.33 0.40 0.50 0.58 0.72 0.85 0.99
09 10-day 2.30 2.88 3.23 3.80 4.33 4.75 5.74 7.09 8.07 9.54 10.68 11.79 09 5-day 1.90 2.29 2.59 3.00 3.45 3.75 4.48 5.57 6.50 7.91 9.16 10.57 09 72-hr 1.73 2.02 2.25 2.62 3.00 3.27 3.92 4.92 5.75 7.05 8.23 9.40 09 48-hr 1.59 1.87 2.07 2.40 2.76 3.00 3.60 4.52 5.28 6.48 7.58 8.62 09 24-hr 1.44 1.68 1.85 2.12 2.41 2.62 3.16 4.00 4.62 5.79 6.71 7.73 09 18-hr 1.33 1.55 1.71 1.95 2.22 2.41 2.91 3.68 4.25 5.33 6.17 7.11 09 12-hr 1.25 1.46 1.60 1.85 2.10 2.28 2.75 3.48 4.02 5.04 5.84 6.72 09 6-hr 1.08 1.27 1.41 1.60 1.81 1.97 2.37 3.00 3.47 4.34 5.03 5.80 09 3-hr 0.92 1.08 1.21 1.37 1.55 1.68 2.02 2.56 2.96 3.71 4.29 4.95 09 2-hr 0.85 1.00 1.12 1.26 1.43 1.55 1.85 2.36 2.72 3.41 3.96 4.56 09 1-hr 0.68 0.79 0.88 1.00 1.13 1.23 1.49 1.88 2.20 2.72 3.15 3.63 09 30-min 0.53 0.62 0.68 0.78 0.89 0.97 1.17 1.47 1.73 2.14 2.48 2.86 09 15-min 0.39 0.46 0.50 0.58 0.65 0.71 0.85 1.08 1.25 1.56 1.81 2.09 09 10-min 0.32 0.37 0.41 0.47 0.53 0.58 0.70 0.88 1.02 1.27 1.48 1.70 09 5-min 0.18 0.20 0.22 0.26 0.29 0.32 0.38 0.48 0.55 0.69 0.81 0.93
117
Table 1. Continued
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
10 10-day 2.55 3.15 3.58 4.21 4.84 5.26 6.36 7.81 8.90 10.34 11.36 12.50 10 5-day 2.09 2.52 2.83 3.29 3.77 4.10 4.99 6.20 7.21 8.45 9.45 10.82 10 72-hr 1.88 2.25 2.49 2.87 3.30 3.59 4.36 5.48 6.34 7.53 8.54 9.52 10 48-hr 1.75 2.08 2.31 2.65 3.02 3.30 4.00 5.03 5.80 6.93 7.86 8.79 10 24-hr 1.63 1.91 2.10 2.41 2.74 2.97 3.62 4.51 5.21 6.23 7.11 8.27 10 18-hr 1.51 1.77 1.95 2.22 2.52 2.74 3.33 4.15 4.79 5.74 6.54 7.61 10 12-hr 1.42 1.66 1.83 2.10 2.38 2.59 3.15 3.93 4.53 5.42 6.19 7.20 10 6-hr 1.23 1.44 1.58 1.71 2.05 2.23 2.73 3.39 3.91 4.68 5.31 6.21 10 3-hr 1.06 1.23 1.35 1.54 1.75 1.90 2.32 2.89 3.33 3.99 4.55 5.29 10 2-hr 0.97 1.13 1.25 1.43 1.62 1.76 2.14 2.66 3.07 3.68 4.20 4.88 10 1-hr 0.77 0.90 0.99 1.13 1.29 1.40 1.70 2.12 2.45 2.93 3.34 3.89 10 30-min 0.61 0.70 0.77 0.89 1.01 1.10 1.34 1.66 1.93 2.31 2.63 3.06 10 15-min 0.43 0.51 0.56 0.65 0.74 0.80 0.98 1.22 1.41 1.68 1.92 2.23 10 10-min 0.36 0.42 0.46 0.53 0.60 0.65 0.80 0.99 1.14 1.37 1.56 1.82 10 5-min 0.20 0.23 0.25 0.29 0.33 0.36 0.43 0.54 0.62 0.75 0.85 0.99
118
Table 2. Sectional Mean Frequency Distributions for Storm Periods of 5 Minutes to 10 Daysand Recurrence Intervals of 2 Months to 100 Years in Indiana
Sectional code (see figure 1 on page 4)
01- Northwes 06 - East Central02 - North Central 07 - Southwest03 - Northeast 08 - South Central04 - West Central 09- Southeast05 - Central
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
01 10-day 2.07 2.50 2.88 3.38 3.89 4.23 4.84 5.79 6.67 8.03 9.23 10.5801 5-day 1.68 2.01 2.27 2.63 3.03 3.29 3.84 4.70 5.50 6.81 7.99 9.3701 72-hr 1.53 1.80 2.04 2.36 2.71 2.95 3.46 4.24 4.97 6.10 7.17 8.3801 48-hr 1.40 1.64 1.83 2.12 2.44 2.65 3.12 3.87 4.56 5.58 6.52 7.5801 24-hr 1.33 1.55 1.69 1.96 2.23 2.42 2.89 3.61 4.22 5.22 6.10 7.1201 18-hr 1.25 1.45 1.59 1.84 2.09 2.27 2.72 3.39 3.97 4.91 5.73 6.6901 12-hr 1.16 1.35 1.48 1.71 1.94 2.11 2.51 3.14 3.67 4.54 5.31 6.1901 6-hr 1.00 1.16 1.27 1.47 1.67 1.82 2.17 2.71 3.16 3.91 4.57 5.3401 3-hr 0.85 0.99 1.08 1.26 1.43 1.55 1.85 2.31 2.70 3.34 3.90 4.5601 2-hr 0.77 0.90 0.98 1.13 1.29 1.40 1.68 2.09 2.45 3.03 3.54 4.1301 1-hr 0.63 0.73 0.80 0.92 1.05 1.14 1.36 1.70 1.98 2.45 2.87 3.3501 30-min 0.50 0.58 0.63 0.73 0.83 0.90 1.07 1.34 1.56 1.93 2.26 2.6301 15-min 0.36 0.42 0.45 0.53 0.60 0.65 0.78 0.97 1.14 1.41 1.65 1.9201 10-min 0.28 0.33 0.36 0.41 0.47 0.51 0.61 0.76 0.89 1.10 1.28 1.5001 5-min 0.16 0.19 0.20 0.23 0.27 0.29 0.35 0.43 0.51 0.63 0.73 0.85
02 10-day 2.04 2.45 2.83 3.33 3.83 4.16 4.75 5.64 6.45 7.69 8.80 10.0302 5-day 1.68 2.01 2.28 2.64 3.04 3.30 3.80 4.62 5.38 6.57 7.63 8.8502 72-hr 1.48 1.74 1.97 2.28 2.62 2.85 3.33 4.10 4.79 5.88 6.86 8.0002 48-hr 1.37 1.60 1.78 2.06 2.37 2.58 3.02 3.73 4.36 5.36 6.25 7.2802 24-hr 1.30 1.51 1.65 1.91 2.17 2.36 2.78 3.43 4.00 4.90 5.67 6.5402 18-hr 1.22 1.42 1.55 1.80 2.04 2.22 2.61 3.22 3.76 4.61 5.33 6.1502 12-hr 1.13 1.31 1.43 1.66 1.89 2.05 2.42 2.98 3.48 4.26 4.93 5.6902 6-hr 0.97 1.13 1.24 1.43 1.63 1.77 2.09 2.57 3.00 3.68 4.25 4.9002 3-hr 0.83 0.97 1.06 1.22 1.39 1.51 1.78 2.20 2.56 3.14 3.63 4.1902 2-hr 0.75 0.88 0.96 1.11 1.26 1.37 1.61 1.99 2.32 2.84 3.29 3.7902 1-hr 0.61 0.71 0.78 0.90 1.02 1.11 1.31 1.61 1.88 2.30 2.66 3.0702 30-min 0.48 0.56 0.61 0.70 0.80 0.87 1.03 1.27 1.48 1.81 2.10 2.4202 15-min 0.35 0.41 0.45 0.52 0.59 0.64 0.75 0.93 1.08 1.32 1.53 1.7702 10-min 0.28 0.32 0.35 0.41 0.46 0.50 0.58 0.72 0.84 1.03 1.19 1.3702 5-min 0.15 0.18 0.20 0.23 0.26 0.28 0.33 0.41 0.48 0.59 0.68 0.78
03 10-day 1.81 2.18 2.52 2.96 3.40 3.70 4.25 5.12 5.84 6.96 8.01 9.1603 5-day 1.52 1.82 2.06 2.38 2.74 2.98 3.46 4.18 4.81 5.83 6.76 7.8003 72-hr 1.35 1.59 1.79 2.08 2.39 2.60 3.01 3.68 4.27 5.21 6.06 7.0103 48-hr 1.27 1.48 1.65 1.91 2.20 2.39 2.77 3.38 3.92 4.78 5.57 6.4503 24-hr 1.19 1.38 1.51 1.75 1.99 2.16 2.52 3.04 3.52 4.29 5.02 5.7703 18-hr 1.12 1.30 1.42 1.64 1.87 2.03 2.37 2.86 3.31 4.03 4.72 5.4203 12-hr 1.03 1.20 1.32 1.52 1.73 1.68 2.19 2.64 3.06 3.73 4.37 5.0203 6-hr 0.89 1.04 1.13 1.31 1.49 1.62 1.89 2.28 2.64 3.22 3.76 4.3303 3-hr 0.76 0.88 0.97 1.12 1.27 1.38 1.61 1.95 2.25 2.75 3.21 3.6903 2-hr 0.69 0.80 0.88 1.01 1.15 1.25 1.46 1.76 2.04 2.49 2.91 3.3503 1-hr 0.56 0.65 0.71 0.83 0.94 1.02 1.18 1.43 1.65 2.02 2.36 2.7103 30-min 0.44 0.51 0.56 0.65 0.74 0.80 0.93 1.12 1.30 1.59 1.86 2.1303 15-min 0.32 0.37 0.41 0.47 0.53 0.58 0.68 0.82 0.95 1.16 1.36 1.5603 10-min 0.25 0.29 0.31 0.36 0.41 0.45 0.53 0.64 0.74 0.90 1.05 1.2103 5-min 0.14 0.17 0.18 0.21 0.24 0.26 0.30 0.36 0.42 0.51 0.60 0.69
119
Table 2. Continued
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
04 10-day 2.32 2.80 3.22 3.79 4.36 4.74 5.43 6.47 7.33 8.50 9.48 10.6504 5-day 1.85 2.21 2.50 2.90 3.34 3.63 4.24 5.15 5.97 7.25 8.31 9.5504 72-hr 1.64 1.93 2.18 2.53 2.91 3.16 3.76 4.53 5.34 6.43 7.45 8.5504 48-hr 1.53 1.79 1.99 2.30 2.65 2.88 3.38 4.12 4.75 5.77 6.66 7.6504 24-hr 1.45 1.68 1.84 2.13 2.42 2.63 3.12 3.83 4.47 5.39 6.17 7.0104 18-hr 1.36 1.58 1.73 2.00 2.27 2.47 2.93 3.60 4.20 5.07 5.80 6.5904 12-hr 1.26 1.47 1.60 1.85 2.11 2.29 2.71 3.33 3.89 4.69 5.37 6.1004 6-hr 1.08 1.26 1.38 1.60 1.81 1.97 2.34 2.87 3.35 4.04 4.63 5.2604 3-hr 0.92 1.08 1.18 1.36 1.55 1.68 2.00 2.45 2.86 3.45 3.95 4.4904 2-hr 0.84 0.98 1.07 1.24 1.41 1.53 1.81 2.22 2.59 3.13 3.58 4.0704 1-hr 0.68 0.79 0.87 1.00 1.14 1.24 1.47 1.80 2.10 2.53 2.90 3.2904 30-min 0.53 0.62 0.68 0.79 0.89 0.97 1.15 1.42 1.65 1.99 2.28 2.5904 15-min 0.39 0.45 0.50 0.58 0.65 0.71 0.84 1.03 1.21 1.46 1.67 1.8904 10-min 0.30 0.35 0.38 0.45 0.51 0.55 0.66 0.80 0.94 1.13 1.30 1.4704 5-min 0.18 0.20 0.22 0.26 0.29 0.32 0.37 0.46 0.54 0.65 0.74 0.84
05 10-day 2.13 2.56 2.95 3.47 3.99 4.34 5.06 6.07 6.96 8.36 9.57 10.8605 5-day 1.73 2.07 2.34 2.71 3.12 3.39 3.97 4.86 5.66 6.91 8.07 9.4405 72-hr 1.52 1.79 2.02 2.34 2.70 2.93 3.45 4.27 5.04 6.15 7.17 8.3105 48-hr 1.42 1.66 1.85 2.14 2.47 2.68 3.18 3.94 4.63 5.65 6.56 7.5505 24-hr 1.35 1.57 1.72 1.99 2.26 2.46 2.92 3.64 4.25 5.16 5.95 6.8405 18-hr 1.27 1.48 1.62 1.87 2.13 2.31 2.74 3.42 3.99 4.85 5.59 6.4305 12-hr 1.18 1.37 1.50 1.73 1.97 2.14 2.54 3.17 3.70 4.49 5.18 5.9505 6-hr 1.02 1.18 1.29 1.50 1.70 1.85 2.19 2.73 3.19 3.87 4.46 5.1305 3-hr 0.86 1.00 1.10 1.27 1.44 1.57 1.87 2.33 2.72 3.30 3.81 4.3805 2-hr 0.79 0.92 1.00 1.16 1.32 1.43 1.69 2.11 2.46 2.99 3.45 3.9705 1-hr 0.64 0.74 0.81 0.94 1.07 1.16 1.37 1.71 2.00 2.43 2.80 3.2105 30-min 0.50 0.58 0.64 0.74 0.84 0.91 1.08 1.35 1.57 1.91 2.20 2.5305 15-min 0.36 0.42 0.46 0.53 0.61 0.66 0.79 0.98 1.15 1.39 1.61 1.8505 10-min 0.29 0.33 0.36 0.42 0.48 0.52 0.61 0.76 0.89 1.08 1.25 1.4405 5-min 0.17 0.19 0.21 0.24 0.28 0.30 0.35 0.44 0.51 0.62 0.71 0.82
06 10-day 2.13 2.57 2.96 3.48 4.00 4.35 5.00 6.00 6.82 8.30 9.55 11.0506 5-day 1.62 1.93 2.19 2.54 2.92 3.17 3.75 4.68 5.50 6.90 8.20 9.6806 72-hr 1.45 1.70 1.92 2.22 2.56 2.78 3.30 4.15 4.98 6.06 7.25 8.5506 48-hr 1.36 1.59 1.77 2.06 2.36 2.57 3.01 3.73 4.40 5.54 6.55 7.7006 24-hr 1.26 1.47 1.61 1.66 2.12 2.30 2.76 3.37 3.89 4.65 5.29 6.0506 18-hr 1.19 1.38 1.51 1.75 1.99 2.16 2.59 3.17 3.66 4.37 4.97 5.6906 12-hr 1.10 1.28 1.40 1.62 1.84 2.00 2.40 2.93 3.38 4.05 4.60 5.2606 6-hr 0.95 1.10 1.20 1.39 1.58 1.72 2.07 2.53 2.92 3.49 3.97 4.5406 3-hr 0.81 0.94 1.03 1.19 1.35 1.47 1.77 2.16 2.49 2.98 3.39 3.8706 2-hr 0.73 0.85 0.93 1.08 1.22 1.33 1.60 1.95 2.26 2.70 3.07 3.5106 1-hr 0.59 0.69 0.76 0.87 0.99 1.08 1.30 1.58 1.83 2.19 2.49 2.8406 30-min 0.47 0.54 0.60 0.69 0.78 0.85 1.02 1.25 1.44 1.72 1.96 2.2406 15-min 0.34 0.40 0.43 0.50 0.57 0.62 0.75 0.91 1.05 1.26 1.43 1.6306 10-min 0.26 0.31 0.34 0.39 0.44 0.48 0.58 0.71 0.82 0.98 1.11 1.2706 5-min 0.15 0.18 0.20 0.23 0.26 0.28 0.33 0.40 0.47 0.56 0.63 0.73
120
Table 2. Continued
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
07 10-day 2.53 3.05 3.52 4.14 4.76 5.17 5.99 7.29 8.46 10.28 11.91 13.7407 5-day 1.96 2.35 2.66 3.08 3.54 3.85 4.54 5.64 6.66 8.25 9.72 11.3207 72-hr 1.80 2.11 2.39 2.77 3.18 3.46 4.10 5.12 6.02 7.49 8.79 10.2807 48-hr 1.65 1.93 2.15 2.50 2.87 3.12 3.68 4.56 5.35 6.62 7.77 9.08'07 24-hr 1.52 1.77 1.93 2.24 2.54 2.76 3.27 4.00 4.65 5.66 6.52 7.4707 18-hr 1.42 1.66 1.81 2.10 2.38 2.59 3.07 3.76 4.37 5.32 6.13 7.0207 12-hr 1.32 1.54 1.68 1.94 2.21 2.40 2.84 3.48 4.05 4.92 5.67 6.5007 6-hr 1.14 1.32 1.45 1.68 1.90 2.07 2.45 3.00 3.49 4.24 4.89 5.6007 3-hr 0.97 1.13 1.24 1.43 1.63 1.77 2.09 2.56 2.98 3.62 4.17 4.7807 2-hr 0.88 1.02 1.12 1.30 1.47 1.60 1.90 2.32 2.70 3.28 3.78 4.3307 1-hr 0.71 0.83 0.91 1.05 1.20 1.30 1.54 1.88 2.19 2.66 3.06 3.5107 30-min 0.56 0.65 0.71 0.83 0.94 1.02 1.21 1.48 1.72 2.09 2.41 2.7607 15-min 0.41 0.48 0.52 0.61 0.69 0.75 0.88 1.08 1.26 1.53 1.76 2.0207 10-min 0.32 0.37 0.41 0.47 0.53 0.58 0.69 0.84 0.98 1.19 1.37 1.5707 5-min 0.18 0.21 0.23 0.27 0.30 0.33 0.39 0.48 0.56 0.68 0.78 0.90
08 10-day 2.39 2.88 3.32 3.90 4.49 4.88 5.74 6.95 7.99 9.60 11.04 12.6408 5-day 1.90 2.27 2.57 2.98 3.42 3.72 4.50 5.54 6.43 7.71 8.88 10.1808 72-hr 1.70 1.99 2.25 2.61 3.00 3.26 3.88 4.82 5.65 6.92 7.99 9.1408 48-hr 1.61 1.88 2.10 2.43 2.80 3.04 3.61 4.41 5.13 6.18 7.14 8.1308 24-hr 1.48 1.72 1.88 2.18 2.47 2.69 3.17 3.90 4.49 5.40 6.15 7.0608 18-hr 1.39 1.62 1.77 2.05 2.33 2.53 2.98 3.67 4.22 5.08 5.78 6.6408 12-hr 1.29 1.50 1.64 1.90 2.15 2.34 2.76 3.39 3.91 4.70 5.35 6.1408 6-hr 1.11 1.29 1.41 1.64 1.66 2.02 2.38 2.93 3.37 4.05 4.61 5.3008 3-hr 0.95 1.10 1.20 1.39 1.58 1.72 2.03 2.50 2.87 3.46 3.94 4.5208 2-hr 0.86 1.00 1.09 1.26 1.44 1.56 1.84 2.26 2.60 3.13 3.57 4.0908 1-hr 0.69 0.81 0.88 1.02 1.16 1.26 1.49 1.83 2.11 2.54 2.89 3.3208 30-min 0.55 0.64 0.70 0.81 0.92 1.00 1.17 1.44 1.66 2.00 2.28 2.6108 15-min 0.40 0.47 0.51 0.59 0.67 0.73 0.86 1.05 1.21 1.46 1.66 1.9108 10-min 0.31 0.36 0.39 0.45 0.52 0.56 0.67 0.82 0.94 1.13 1.29 1.4808 5-min 0.18 0.20 0.22 0.26 0.29 0.32 0.38 0.47 0.54 0.65 0.74 0.85
09 10-day 2.35 2.83 3.26 3.83 4.41 4.79 5.62 6.85 7.87 9.42 10.90 12.3309 5-day 1.86 2.23 2.52 2.92 3.36 3.65 4.29 5.22 6.06 7.39 8.54 9.9009 72-hr 1.67 1.96 2.22 2.58 2.96 3.22 3.87 4.77 5.53 6.75 7.80 8.9509 48-hr 1.55 1.82 2.02 2.34 2.70 2.93 3.53 4.40 5.13 6.22 7.19 8.2009 24-hr 1.36 1.58 1.73 2.00 2.27 2.47 3.03 3.81 4.42 5.39 6.20 7.1209 18-hr 1.28 1.48 1.62 1.88 2.13 2.32 2.85 3.58 4.15 5.07 5.83 6.6909 12-hr 1.18 1.38 1.50 1.74 1.98 2.15 2.64 3.31 3.85 4.69 5.39 6.1909 6-hr 1.02 1.18 1.29 1.50 1.70 1.85 2.27 2.86 3.32 4.04 4.65 5.3409 3-hr 0.87 1.01 1.11 1.28 1.45 1.58 1.94 2.44 2.83 3.45 3.97 4.5609 2-hr 0.79 0.92 1.00 1.16 1.32 1.43 1.76 2.21 2.56 3.13 3.60 4.1309 1-hr 0.64 0.74 0.81 0.94 1.07 1.16 1.42 1.79 2.08 2.53 2.91 3.3509 30-min 0.50 0.58 0.64 0.74 0.84 0.91 1.12 1.41 1.64 1.99 2.29 2.6309 15-min 0.37 0.43 0.47 0.54 0.62 0.67 0.82 1.03 1.19 1.46 1.67 1.9209 10-min 0.29 0.33 0.36 0.42 0.48 0.52 0.64 0.80 0.93 1.13 1.30 1.5009 5-min 0.17 0.19 0.21 0.24 0.26 0.30 0.36 0.46 0.53 0.65 0.74 0.85
121
Table 3. Sectional Mean Frequency Distributions for Storm Periods of 5 Minutes to 10 Daysand Recurrence Intervals of 2 Months to 100 Years in Iowa
Sectional code (see figure 1 on page 4)
01 - Northwest 06 - East Central02 - North Central 07 - Southwest03 - Northeast 08 - South Central04 - West Central 09 - Southeast05 - Central
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
01 10-day 1.98 2.39 2.75 3.24 3.73 4.05 4.81 5.84 6.70 8.02 9.11 10.3101 5-day 1.59 1.90 2.15 2.49 2.86 3.11 3.77 4.68 5.43 6.61 7.60 8.7501 72-hr 1.41 1.66 1.88 2.18 2.50 2.72 3.33 4.21 4.99 6.07 7.12 8.2301 48-hr 1.32 1.55 1.73 2.00 2.30 2.50 3.01 3.81 4.52 5.60 6.53 7.5201 24-hr 1.22 1.42 1.55 1.80 2.04 2.22 2.75 3.50 4.14 5.11 5.97 6.9201 18-hr 1.15 1.34 1.46 1.69 1.92 2.09 2.59 3.29 3.89 4.80 5.61 6.5001 12-hr 1.06 1.24 1.35 1.56 1.78 1.93 2.39 3.05 3.60 4.45 5.19 6.0201 6-hr 0.91 1.06 1.16 1.34 1.53 1.66 2.06 2.62 3.11 3.83 4.48 5.1901 3-hr 0.78 0.91 0.99 1.15 1.31 1.42 1.76 2.24 2.65 3.27 3.82 4.4301 2-hr 0.71 0.83 0.90 1.04 1.19 1.29 1.59 2.03 2.40 2.96 3.46 4.0101 1-hr 0.57 0.67 0.73 0.84 0.96 1.04 1.29 1.64 1.95 2.40 2.81 3.2501 30-min 0.45 0.52 0.57 0.66 0.75 0.82 1.02 1.30 1.53 1.89 2.21 2.5601 15-min 0.33 0.38 0.42 0.49 0.55 0.60 0.74 0.95 1.12 1.38 1.61 1.8701 10-min 0.26 0.30 0.33 0.38 0.43 0.47 0.58 0.73 0.87 1.07 1.25 1.4501 6-min 0.15 0.17 0.19 0.22 0.25 0.27 0.33 0.42 0.50 0.61 0.72 0.83
02 10-day 1.96 2.37 2.73 3.21 3.69 4.01 5.04 6.26 7.32 8.93 10.37 11.4002 5-day 1.75 2.10 2.37 2.75 3.16 3.44 4.13 5.05 5.80 7.00 8.03 9.2802 72-hr 1.49 1.74 1.97 2.29 2.63 2.86 3.53 4.45 5.15 6.33 7.30 8.3002 48-hr 1.42 1.66 1.84 2.14 2.46 2.67 3.30 4.11 4.78 5.80 6.67 7.6702 24-hr 1.30 1.51 1.65 1.91 2.17 2.36 2.98 3.72 4.38 5.33 6.14 7.0702 18-hr 1.22 1.42 1.55 1.80 2.04 2.22 2.80 3.50 4.12 5.01 5.77 6.6502 12-hr 1.13 1.31 1.43 1.66 1.89 2.05 2.59 3.24 3.81 4.64 5.34 6.1502 6-hr 0.97 1.13 1.24 1.43 1.63 1.77 2.24 2.79 3.29 4.00 4.61 5.3002 3-hr 0.83 0.97 1.06 1.22 1.39 1.51 1.91 2.38 2.80 3.41 3.93 4.5202 2-hr 0.75 0.88 0.96 1.11 1.26 1.37 1.73 2.16 2.54 3.09 3.56 4.1002 1-hr 0.61 0.71 0.78 0.90 1.02 1.11 1.40 1.75 2.06 2.51 2.89 3.3202 30-min 0.48 0.56 0.61 0.70 0.80 0.87 1.10 1.38 1.62 1.97 2.27 2.6202 15-min 0.35 0.41 0.45 0.52 0.59 0.64 0.80 1.00 1.18 1.44 1.66 1.9102 10-min 0.28 0.32 0.35 0.41 0.46 0.50 0.63 0.78 0.92 1.12 1.29 1.4802 5-min 0.15 0.18 0.20 0.23 0.26 0.28 0.36 0.45 0.53 0.64 0.74 0.85
03 10-day 2.07 2.49 2.87 3.38 3.88 4.22 5.04 6.17 7.07 8.29 9.20 10.1903 5-day 1.69 2.03 2.29 2.66 3.05 3.32 3.94 4.86 5.64 6.84 7.75 8.7703 72-hr 1.49 1.74 1.97 2.29 2.63 2.86 3.44 4.33 5.14 6.19 7.00 7.8403 48-hr 1.37 1.61 1.79 2.07 2.38 2.59 3.20 4.02 4.69 5.62 6.34 7.0903 24-hr 1.28 1.48 1.62 1.88 2.13 2.32 2.91 3.67 4.31 5.11 5.73 6.3603 18-hr 1.20 1.40 1.53 1.77 2.01 2.18 2.74 3.45 4.05 4.80 5.39 5.9803 12-hr 1.11 1.29 1.41 1.64 1.86 2.02 2.53 3.19 3.75 4.45 4.99 5.5303 6-hr 0.96 1.11 1.22 1.41 1.60 1.74 2.18 2.75 3.23 3.83 4.30 4.7703 3-hr 0.81 0.95 1.04 1.20 1.36 1.48 1.86 2.35 2.76 3.27 3.67 4.0703 2-hr 0.74 0.86 0.94 1.09 1.24 1.35 1.69 2.13 2.50 2.96 3.32 3.6903 1-hr 0.60 0.70 0.76 0.88 1.00 1.09 1.37 1.72 2.03 2.40 2.69 2.9903 30-min 0.47 0.55 0.60 0.70 0.79 0.86 1.08 1.36 1.59 1.89 2.12 2.3503 15-min 0.35 0.40 0.44 0.51 0.58 0.63 0.79 0.99 1.16 1.38 1.55 1.7203 10-min 0.27 0.31 0.34 0.40 0.45 0.49 0.61 0.77 0.91 1.07 1.20 1.3403 5-min 0.15 0.18 0.20 0.23 0.26 0.28 0.35 0.44 0.52 0.61 0.69 0.76
122
Table 3. Continued
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
04 10-day 2.15 2.59 2.99 3.51 4.04 4.39 5.22 6.31 7.16 8.24 9.21 10.2704 5-day 1.76 2.11 2.39 2.77 3.18 3.46 4.06 4.94 5.74 7.04 8.13 9.2704 72-hr 1.52 1.79 2.02 2.34 2.70 2.93 3.51 4.37 5.13 6.28 7.26 8.4604 48-hr 1.43 1.67 1.86 2.15 2.47 2.69 3.16 3.97 4.71 5.86 6.81 7.8204 24-hr 1.36 1.59 1.74 2.01 2.28 2.48 2.94 3.64 4.30 5.27 6.08 7.0004 18-hr 1.28 1.49 1.63 1.89 2.14 2.33 2.76 3.42 4.04 4.95 5.72 6.5804 12-hr 1.19 1.38 1.51 1.75 1.99 2.16 2.56 3.17 3.74 4.58 5.29 6.0904 6-hr 1.02 1.19 1.30 1.51 1.71 1.86 2.20 2.73 3.23 3.95 4.56 5.2504 3-hr 0.87 1.02 1.11 1.29 1.46 1.59 1.88 2.33 2.75 3.37 3.89 4.4804 2-hr 0.79 0.92 1.01 1.17 1.32 1.44 1.71 2.11 2.49 3.06 3.53 4.0604 1-hr 0.64 0.75 0.82 0.95 1.08 1.17 1.38 1.71 2.02 2.48 2.86 3.2904 30-min 0.51 0.59 0.64 0.75 0.85 0.92 1.09 1.35 1.59 1.95 2.25 2.5904 15-min 0.37 0.43 0.47 0.54 0.62 0.67 0.79 0.98 1.16 1.42 1.64 1.8904 10-min 0.29 0.33 0.36 0.42 0.48 0.52 0.62 0.76 0.90 1.11 1.28 1.4704 5-min 0.17 0.19 0.21 0.24 0.28 0.30 0.35 0.44 0.52 0.63 0.73 0.84
05 10-day 2.20 2.64 3.05 3.58 4.12 4.48 5.20 6.22 7.22 8.61 9.66 10.8805 5-day 1.76 2.11 2.39 2.77 3.18 3.46 4.05 4.94 5.72 6.92 7.98 9.1805 72-hr 1.51 1.77 2.00 2.32 2.67 2.90 3.47 4.41 5.16 6.22 7.06 8.1205 48-hr 1.40 1.64 1.82 2.11 2.43 2.64 3.13 3.93 4.67 5.75 6.52 7.3305 24-hr 1.31 1.52 1.67 1.93 2.19 2.38 2.91 3.64 4.27 5.15 5.87 6.6105 18-hr 1.23 1.43 1.57 1.81 2.06 2.24 2.74 3.42 4.01 4.84 5.52 6.2105 12-hr 1.14 1.32 1.45 1.68 1.90 2.07 2.53 3.17 3.71 4.48 5.11 5.7505 6-hr 0.98 1.15 1.25 1.45 1.65 1.79 2.18 2.73 3.20 3.86 4.40 4.9605 3-hr 0.84 0.97 1.06 1.23 1.40 1.52 1.86 2.33 2.73 3.30 3.76 4.2305 2-hr 0.76 0.88 0.97 1.12 1.27 1.38 1.69 2.11 2.48 2.99 3.40 3.8305 1-hr 0.62 0.72 0.78 0.91 1.03 1.12 1.37 1.71 2.01 2.42 2.76 3.1105 30-min 0.48 0.56 0.62 0.71 0.81 0.88 1.08 1.35 1.58 1.91 2.17 2.4505 15-min 0.35 0.41 0.45 0.52 0.59 0.64 0.79 0.98 1.15 1.39 1.58 1.7805 10-min 0.28 0.32 0.35 0.41 0.46 0.50 0.61 0.76 0.90 1.08 1.23 1.3905 5-min 0.16 0.19 0.20 0.23 0.27 0.29 0.35 0.44 0.51 0.62 0.70 0.79
06 10-day 2.14 2.57 2.96 3.49 4.01 4.36 5.21 6.27 7.12 8.25 9.27 10.3506 5-day 1.84 2.20 2.48 2.88 3.31 3.60 4.12 4.89 5.61 6.70 7.75 9.0006 72-hr 1.57 1.84 2.08 2.41 2.77 3.01 3.59 4.53 5.31 6.42 7.35 8.4206 48-hr 1.38 1.61 1.79 2.08 2.39 2.60 3.21 4.15 5.05 6.02 6.87 7.8306 24-hr 1.32 1.54 1.68 1.94 2.21 2.40 3.06 3.84 4.44 5.42 6.25 7.1306 18-hr 1.24 1.45 1.58 1.83 2.08 2.26 2.88 3.61 4.17 5.09 5.88 6.7006 12-hr 1.15 1.34 1.46 1.69 1.92 2.09 2.66 3.34 3.86 4.72 5.44 6.2006 6-hr 0.99 1.15 1.26 1.46 1.66 1.80 2.30 2.88 3.33 4.07 4.69 5.3506 3-hr 0.85 0.99 1.08 1.25 1.42 1.54 1.96 2.46 2.84 3.47 4.00 4.5606 2-hr 0.76 0.89 0.97 1.13 1.28 1.39 1.77 2.23 2.58 3.14 3.62 4.1406 1-hr 0.62 0.72 0.79 0.92 1.04 1.13 1.44 1.80 2.09 2.55 2.94 3.3506 30-min 0.49 0.57 0.62 0.72 0.82 0.89 1.13 1.42 1.64 2.01 2.31 2.6406 15-min 0.36 0.42 0.45 0.53 0.60 0.65 0.83 1.04 1.20 1.46 1.69 1.9306 10-min 0.28 0.32 0.35 0.41 0.46 0.50 0.64 0.81 0.93 1.14 1.31 1.5006 5-min 0.16 0.19 0.20 0.23 0.27 0.29 0.37 0.46 0.53 0.65 0.75 0.86
123
Table 3. Continued
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
07 10-day 2.29 2.76 3.18 3.74 4.30 4.67 5.47 6.54 7.53 9.00 10.25 11.6607 5-day 1.81 2.17 2.45 2.84 3.27 3.55 4.26 5.30 6.20 7.59 8.71 9.8607 72-hr 1.65 1.94 2.19 2.54 2.93 3.18 3.85 4.79 5.56 6.78 7.80 8.9907 48-hr 1.57 1.84 2.05 2.38 2.73 2.97 3.53 4.38 5.11 6.19 7.09 8.0407 24-hr 1.52 1.77 1.93 2.24 2.54 2.76 3.22 3.93 4.57 5.56 6.45 7.2807 18-hr 1.42 1.66 1.81 2.10 2.38 2.59 3.03 3.69 4.30 5.23 6.06 6.8407 12-hr 1.32 1.54 1.68 1.94 2.21 2.40 2.80 3.42 3.98 4.84 5.61 6.3307 6-hr 1.14 1.32 1.45 1.68 1.90 2.07 2.41 2.95 3.43 4.17 4.84 5.4607 3-hr 0.97 1.13 1.24 1.43 1.63 1.77 2.06 2.52 2.92 3.56 4.13 4.6607 2-hr 0.88 1.02 1.12 1.30 1.47 1.60 1.87 2.28 2.65 3.22 3.74 4.2207 1-hr 0.71 0.83 0.91 1.05 1.20 1.30 1.51 1.85 2.15 2.61 3.03 3.4207 30-min 0.56 0.65 0.71 0.83 0.94 1.02 1.19 1.45 1.69 2.06 2.39 2.6907 15-min 0.41 0.48 0.52 0.61 0.69 0.75 0.87 1.06 1.23 1.50 1.74 1.9707 10-min 0.32 0.37 0.41 0.47 0.53 0.58 0.68 0.83 0.96 1.17 1.35 1.5307 5-min 0.18 0.21 0.23 0.27 0.30 0.33 0.39 0.47 0.55 0.67 0.77 0.87
08 10-day 2.28 2.74 3.16 3.72 4.28 4.65 5.45 6.61 7.57 8.99 10.09 11.0408 5-day 1.81 2.17 2.45 2.84 3.27 3.55 4.32 5.37 6.26 7.64 8.78 9.9908 72-hr 1.60 1.88 2.13 2.46 2.83 3.08 3.67 4.68 5.64 6.90 7.96 9.2408 48-hr 1.48 1.74 1.93 2.24 2.58 2.80 3.39 4.30 5.06 6.28 7.35 8.6008 24-hr 1.38 1.60 1.75 2.03 2.30 2.50 3.11 3.87 4.65 5.78 6.73 7.7408 18-hr 1.29 1.50 1.64 1.90 2.16 2.35 2.92 3.64 4.37 5.43 6.33 7.2808 12-hr 1.19 1.39 1.52 1.76 2.00 2.17 2.71 3.37 4.05 5.03 5.86 6.7308 6-hr 1.03 1.20 1.32 1.52 1.73 1.88 2.33 2.90 3.49 4.34 5.05 5.8008 3-hr 0.88 1.02 1.12 1.30 1.47 1.60 1.99 2.48 2.98 3.70 4.31 4.9508 2-hr 0.80 0.93 1.01 1.17 1.33 1.45 1.80 2.24 2.70 3.35 3.90 4.4908 1-hr 0.64 0.75 0.82 0.95 1.08 1.17 1.46 1.82 2.19 2.72 3.16 3.6408 30-min 0.51 0.60 0.65 0.75 0.86 0.93 1.15 1.43 1.72 2.14 2.49 2.8608 15-min 0.37 0.44 0.48 0.55 0.63 0.68 0.84 1.04 1.26 1.56 1.82 2.0908 10-min 0.29 0.33 0.36 0.42 0.48 0.52 0.65 0.81 0.98 1.21 1.41 1.6308 5-min 0.17 0.19 0.21 0.24 0.28 0.30 0.37 0.46 0.56 0.69 0.81 0.93
09 10-day 2.19 2.64 3.04 3.58 4.11 4.47 5.44 6.50 7.35 8.45 9.33 10.4209 5-day 1.78 2.13 2.41 2.79 3.21 3.49 4.31 5.45 6.32 7.60 8.69 9.9509 72-hr 1.55 1.82 2.06 2.38 2.74 2.98 3.79 4.87 5.74 6.95 7.88 8.9809 48-hr 1.48 1.73 1.93 2.23 2.57 2.79 3.50 4.46 5.20 6.35 7.32 8.4009 24-hr 1.38 1.60 1.75 2.03 2.30 2.50 3.14 4.03 4.67 5.67 6.58 7.5909 18-hr 1.29 1.50 1.64 1.90 2.16 2.35 2.95 3.79 4.39 5.33 6.19 7.1309 12-hr 1.19 1.39 1.52 1.76 2.00 2.17 2.73 3.51 4.06 4.93 5.72 6.6009 6-hr 1.03 1.20 1.32 1.52 1.73 1.88 2.36 3.02 3.50 4.25 4.93 5.6909 3-hr 0.88 1.02 1.12 1.30 1.47 1.60 2.01 2.58 2.99 3.63 4.21 4.8609 2-hr 0.80 0.93 1.01 1.17 1.33 1.45 1.82 2.34 2.71 3.29 3.82 4.4009 1-hr 0.64 0.75 0.82 0.95 1.08 1.17 1.48 1.89 2.19 2.66 3.09 3.5709 30-min 0.51 0.60 0.65 0.75 0.86 0.93 1.16 1.49 1.73 2.10 2.43 2.8109 15-min 0.37 0.44 0.48 0.55 0.63 0.68 0.85 1.09 1.26 1.53 1.78 2.0509 10-min 0.29 0.33 0.36 0.42 0.48 0.52 0.66 0.85 0.98 1.19 1.38 1.5909 5-min 0.17 0.19 0.21 0.24 0.28 0.30 0.38 0.48 0.56 0.68 0.79 0.91
124
Table 4. Sectional Mean Frequency Distributions for Storm Periods of 5 Minutes to 10 Daysand Recurrence Intervals of 2 Months to 100 Years in Kentucky
Sectional code (see figure 1 on page 4)
01 – Western 03 - Eastern02 - Central 04 - Bluegrass
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
01 10-day 2.57 3.09 3.56 4.19 4.82 5.24 6.27 7.74 8.94 9.99 10.60 11.1201 5-day 2.18 2.61 2.95 3.42 3.94 4.28 5.09 6.35 7.42 8.90 9.82 10.5301 72-hr 1.94 2.28 2.58 2.99 3.44 3.74 4.50 5.53 6.41 7.62 8.67 9.6801 48-hr 1.80 2.11 2.35 2.72 3.13 3.40 4.09 5.10 5.90 6.92 7.84 8.7401 24-hr 1.71 1.98 2.17 2.51 2.85 3.10 3.75 4.66 5.39 6.38 7.19 8.0901 18-hr 1.60 1.86 2.04 2.36 2.68 2.91 3.53 4.38 5.07 6.00 6.76 7.6001 12-hr 1.49 1.73 1.89 2.19 2.48 2.70 3.26 4.05 4.69 5.55 6.26 7.0401 6-hr 1.28 1.48 1.62 1.88 2.13 2.32 2.81 3.49 4.04 4.78 5.39 6.0701 3-hr 1.09 1.27 1.39 1.60 1.82 1.98 2.40 2.98 3.45 4.08 4.60 5.1801 2-hr 0.99 1.15 1.26 1.46 1.66 1.80 2.17 2.70 3.13 3.70 4.17 4.6901 1-hr 0.80 0.93 1.02 1.18 1.34 1.46 1.76 2.19 2.53 3.00 3.38 3.8001 30-min 0.63 0.74 0.80 0.93 1.06 1.15 1.39 1.72 1.99 2.36 2.66 2.9901 15-min 0.46 0.54 0.59 0.68 0.77 0.84 1.01 1.26 1.46 1.72 1.94 2.1801 10-min 0.36 0.42 0.45 0.53 0.60 0.65 0.79 0.98 1.13 1.34 1.51 1.7001 5-min 0.20 0.24 0.26 0.30 0.34 0.37 0.45 0.56 0.65 0.77 0.86 0.97
02 10-day 2.52 3.03 3.50 4.11 4.73 5.14 6.03 7.45 8.68 9.86 10.57 11.0502 5-day 2.03 2.43 2.75 3.19 3.67 3.99 4.78 6.00 7.04 8.39 9.35 10.2202 72-hr 1.79 2.10 2.38 2.76 3.17 3.45 4.20 5.26 6.22 7.50 8.46 9.3702 48-hr 1.67 1.96 2.18 2.53 2.91 3.16 3.88 4.82 5.65 6.82 7.75 8.7502 24-hr 1.62 1.88 2.06 2.38 2.70 2.94 3.49 4.34 5.10 6.22 7.09 7.9602 18-hr 1.52 1.77 1.93 2.24 2.54 2.76 3.28 4.08 4.79 5.85 6.66 7.4802 12-hr 1.41 1.64 1.79 2.07 2.36 2.56 3.04 3.78 4.44 5.41 6.17 6.9302 6-hr 1.21 1.41 1.54 1.78 2.02 2.20 2.62 3.26 3.82 4.66 5.32 5.9702 3-hr 1.03 1.20 1.32 1.52 1.73 1.88 2.23 2.78 3.26 3.98 4.54 5.0902 2-hr 0.94 1.09 1.20 1.39 1.57 1.71 2.02 2.52 2.96 3.61 4.11 4.6202 1-hr 0.76 0.88 0.97 1.12 1.27 1.38 1.64 2.04 2.40 2.92 3.33 3.7402 30-min 0.60 0.70 0.76 0.88 1.00 1.09 1.29 1.61 1.89 2.30 2.62 2.9502 15-min 0.43 0.51 0.55 0.64 0.73 0.79 0.94 1.17 1.38 1.68 1.91 2.1502 10-min 0.34 0.40 0.43 0.50 0.57 0.62 0.73 0.91 1.07 1.31 1.49 1.6702 5-min 0.19 0.22 0.24 0.28 0.32 0.35 0.42 0.52 0.61 0.75 0.85 0.96
03 10-day 2.22 2.67 3.08 3.62 4.17 4.53 5.41 6.67 7.69 8.93 9.68 10.4003 5-day 1.82 2.17 2.46 2.85 3.28 3.56 4.26 5.21 6.04 7.11 7.99 8.8603 72-hr 1.60 1.88 2.13 2.46 2.83 3.08 3.68 4.61 5.41 6.36 7.15 7.9903 48-hr 1.48 1.73 1.93 2.23 2.57 2.79 3.37 4.19 4.86 5.76 6.49 7.2303 24-hr 1.41 1.64 1.79 2.07 2.36 2.56 3.05 3.76 4.36 5.15 5.78 6.4403 18-hr 1.33 1.54 1.69 1.95 2.22 2.41 2.87 3.53 4.10 4.84 5.43 6.0503 12-hr 1.23 1.43 1.56 1.81 2.05 2.23 2.65 3.27 3.79 4.48 5.03 5.6003 6-hr 1.06 1.23 1.34 1.56 1.77 1.92 2.29 2.82 3.27 3.86 4.34 4.8303 3-hr 0.90 1.05 1.15 1.33 1.51 1.64 1.95 2.41 2.79 3.30 3.70 4.1203 2-hr 0.81 0.95 1.04 1.20 1.36 1.48 1.77 2.18 2.53 2.99 3.35 3.7403 1-hr 0.66 0.77 0.84 0.97 1.10 1.20 1.43 1.77 2.05 2.42 2.72 3.0303 30-min 0.52 0.61 0.66 0.77 0.87 0.95 1.13 1.39 1.61 1.91 2.14 2.3803 15-min 0.38 0.44 0.48 0.56 0.63 0.69 0.82 1.02 1.18 1.39 1.56 1.7403 10-min 0.30 0.35 0.38 0.44 0.50 0.54 0.64 0.79 0.92 1.08 1.21 1.3503 5-min 0.17 0.20 0.22 0.25 0.29 0.31 0.37 0.45 0.52 0.62 0.69 0.77
125
Table 4. Concluded
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
04 10-day 2.31 2.78 3.21 3.78 4.34 4.72 5.53 6.48 7.27 8.31 9.06 9.7904 5-day 1.83 2.18 2.47 2.86 3.29 3.58 4.25 5.15 5.93 6.95 7.84 8.7704 72-hr 1.62 1.90 2.15 2.50 2.87 3.12 3.71 4.54 5.22 6.14 6.96 7.8604 48-hr 1.54 1.80 2.00 2.32 2.67 2.90 3.42 4.13 4.73 5.60 6.38 7.2304 24-hr 1.46 1.70 1.86 2.15 2.44 2.65 3.09 3.73 4.26 5.06 5.74 6.5304 18-hr 1.37 1.59 1.74 2.02 2.29 2.49 2.90 3.51 4.00 4.76 5.40 6.1404 12-hr 1.27 1.48 1.62 1.87 2.13 2.31 2.69 3.25 3.71 4.40 4.99 5.6804 6-hr 1.09 1.27 1.39 1.61 1.83 1.99 2.32 2.80 3.20 3.80 4.30 4.9004 3-hr 0.94 1.09 1.19 1.38 1.56 1.70 1.98 2.39 2.73 3.24 3.67 4.1804 2-hr 0.85 0.99 1.08 1.25 1.42 1.54 1.79 2.16 2.47 2.93 3.33 3.7904 1-hr 0.69 0.80 0.88 1.01 1.15 1.25 1.45 1.75 2.00 2.38 2.70 3.0704 30-min 0.54 0.63 0.69 0.79 0.90 0.98 1.14 1.38 1.58 1.87 2.12 2.4204 15-min 0.40 0.46 0.50 0.58 0.66 0.72 0.83 1.01 1.15 1.37 1.55 1.7604 10-min 0.31 0.36 0.39 0.45 0.52 0.56 0.65 0.78 0.89 1.06 1.21 1.3704 5-min 0.18 0.20 0.22 0.26 0.29 0.32 0.37 0.45 0.51 0.61 0.69 0.78
126
Table 5. Sectional Mean Frequency Distributions for Storm Periods of 5 Minutes to 10 Days
and Recurrence Intervals of 2 Months to 100 Years in Michigan
Sectional code (see figure 1 on page 4)
01 - West Upper 06 - Central Lower02 - East Upper 07 - East Central Lower03 - Northwest Lower 08 - Southwest Lower04 - Northeast Lower 09 - South Central Lower05 - West Central Lower 10 - Southeast Lower
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
01 10-day 1.69 2.04 2.35 2.76 3.17 3.45 4.28 5.34 6.17 7.27 8.11 8.9901 5-day 1.41 1.69 1.91 2.22 2.55 2.77 3.38 4.23 4.91 5.86 6.65 7.5001 72-hr 1.24 1.46 1.65 1.91 2.20 2.39 2.96 3.69 4.29 5.11 5.79 6.4901 48-hr 1.14 1.33 1.48 1.72 1.98 2.15 2.64 3.31 3.84 4.59 5.20 5.8601 24-hr 1.07 1.25 1.37 1.58 1.79 1.95 2.39 3.00 3.48 4.17 4.73 5.3201 18-hr 1.01 1.17 1.28 1.48 1.68 1.83 2.25 2.82 3.27 3.92 4.45 5.0001 12-hr 0.94 1.09 1.19 1.38 1.56 1.70 2.08 2.61 3.03 3.63 4.12 4.6301 6-hr 0.80 0.93 1.02 1.18 1.34 1.46 1.79 2.25 2.61 3.13 3.55 3.9901 3-hr 0.69 0.80 0.88 1.01 1.15 1.25 1.53 1.92 2.23 2.67 3.03 3.4001 2-hr 0.62 0.72 0.79 0.92 1.04 1.13 1.39 1.74 2.02 2.42 2.74 3.0901 1-hr 0.51 0.59 0.64 0.75 0.85 0.92 1.12 1.41 1.64 1.96 2.22 2.5001 30-min 0.40 0.46 0.50 0.58 0.66 0.72 0.88 1.11 1.29 1.54 1.75 1.9701 15-min 0.29 0.34 0.37 0.43 0.49 0.53 0.65 0.81 0.94 1.13 1.28 1.4401 10-min 0.23 0.26 0.29 0.33 0.38 0.41 0.50 0.63 0.73 0.88 0.99 1.1201 5-min 0.13 0.15 0.16 0.19 0.21 0.23 0.29 0.36 0.42 0.50 0.57 0.64
02 10-day 1.61 1.94 2.23 2.62 3.02 3.28 3.93 4.78 5.44 6.43 7.22 7.9802 5-day 1.25 1.50 1.70 1.97 2.26 2.46 3.00 3.71 4.25 5.11 5.81 6.5502 72-hr 1.15 1.35 1.52 1.77 2.03 2.21 2.62 3.27 3.78 4.57 5.23 5.9402 48-hr 0.97 1.13 1.26 1.46 1.68 1.83 2.31 2.98 3.49 4.24 4.88 5.5502 24-hr 0.91 1.06 1.16 1.34 1.53 1.66 2.09 2.71 3.19 3.87 4.44 5.0302 18-hr 0.86 1.00 1.09 1.26 1.44 1.56 1.96 2.55 3.00 3.64 4.17 4.7302 12-hr 0.79 0.92 1.01 1.17 1.32 1.44 1.82 2.36 2.78 3.37 3.86 4.3802 6-hr 0.69 0.80 0.88 1.01 1.15 1.25 1.57 2.03 2.39 2.90 3.33 3.7702 3-hr 0.58 0.68 0.74 0.86 0.98 1.06 1.34 1.73 2.04 2.48 2.84 3.2202 2-hr 0.53 0.61 0.67 0.78 0.88 0.96 1.21 1.57 1.85 2.24 2.58 2.9202 1-hr 0.43 0.50 0.55 0.63 0.72 0.78 0.98 1.27 1.50 1.82 2.09 2.3602 30-min 0.34 0.39 0.43 0.49 0.56 0.61 0.77 1.00 1.18 1.43 1.64 1.8602 15-min 0.25 0.29 0.31 0.36 0.41 0.45 0.56 0.73 0.86 1.04 1.20 1.3602 10-min 0.19 0.22 0.24 0.28 0.32 0.35 0.44 0.57 0.67 0.81 0.93 1.0602 5-min 0.11 0.13 0.14 0.16 0.18 0.20 0.25 0.33 0.38 0.46 0.53 0.60
03 10-day 1.63 1.96 2.26 2.66 3.06 3.33 3.99 4.92 5.65 6.66 7.50 8.3503 5-day 1.29 1.54 1.75 2.02 2.33 2.53 3.10 3.91 4.57 5.46 6.23 7.0403 72-hr 1.09 1.27 1.44 1.67 1.92 2.09 2.62 3.36 3.96 4.86 5.56 6.3503 48-hr 0.97 1.13 1.26 1.46 1.68 1.83 2.34 3.02 3.55 4.31 4.94 5.6003 24-hr 0.89 1.04 1.13 1.31 1.49 1.62 2.09 2.70 3.21 3.89 4.47 5.0803 18-hr 0.84 0.97 1.06 1.23 1.40 1.52 1.96 2.54 3.02 3.66 4.20 4.7803 12-hr 0.78 0.90 0.99 1.14 1.30 1.41 1.82 2.35 2.79 3.38 3.89 4.4203 6-hr 0.67 0.78 0.85 0.99 1.12 1.22 1.57 2.03 2.41 2.92 3.35 3.8103 3-hr 0.57 0.67 0.73 0.84 0.96 1.04 1.34 1.73 2.05 2.49 2.86 3.2503 2-hr 0.52 0.60 0.66 0.76 0.86 0.94 1.21 1.57 1.86 2.26 2.59 2.9503 1-hr 0.42 0.49 0.53 0.62 0.70 0.76 0.98 1.27 1.51 1.83 2.10 2.3903 30-min 0.33 0.38 0.42 0.49 0.55 0.60 0.77 1.00 1.19 1.44 1.65 1.8803 15-min 0.24 0.28 0.31 0.36 0.40 0.44 0.56 0.73 0.87 1.05 1.21 1.3703 10-min 0.19 0.22 0.24 0.28 0.31 0.34 0.44 0.57 0.67 0.82 0.94 1.0703 5-min 0.10 0.12 0.13 0.15 0.17 0.19 0.25 0.32 0.39 0.47 0.54 0.61
127
Table 5. Continued
Rainfall inches for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
04 10-day 1.56 1.88 2.17 2.55 2.93 3.19 3.77 4.56 5.22 6.10 6.85 7.6004 5-day 1.26 1.51 1.70 1.98 2.27 2.47 2.99 3.68 4.23 4.97 5.58 6.2304 72-hr 1.12 1.31 1.48 1.72 1.98 2.15 2.63 3.27 3.75 4.45 5.00 5.6004 48-hr 1.00 1.17 1.30 1.51 1.74 1.89 2.32 2.88 3.33 3.93 4.43 4.9504 24-hr 0.94 1.09 1.20 1.39 1.57 1.71 2.11 2.62 3.04 3.60 4.06 4.5304 18-hr 0.89 1.03 1.13 1.30 1.48 1.61 1.98 2.46 2.86 3.38 3.82 4.2604 12-hr 0.82 0.95 1.04 1.21 1.37 1.49 1.84 2.28 2.64 3.13 3.53 3.9404 6-hr 0.70 0.82 0.90 1.04 1.18 1.28 1.58 1.96 2.28 2.70 3.05 3.4004 3-hr 0.60 0.70 0.76 0.88 1.00 1.09 1.35 1.68 1.95 2.30 2.60 2.9004 2-hr 0.54 0.63 0.69 0.80 0.91 0.99 1.22 1.52 1.76 2.09 2.35 2.6304 1-hr 0.44 0.51 0.56 0.65 0.74 0.80 0.99 1.23 1.43 1.69 1.91 2.1304 30-min 0.35 0.40 0.44 0.51 0.58 0.63 0.78 0.97 1.12 1.33 1.50 1.6804 15-min 0.25 0.29 0.32 0.37 0.42 0.46 0.57 0.71 0.82 0.97 1.10 1.2204 10-min 0.20 0.23 0.25 0.29 0.33 0.36 0.44 0.55 0.64 0.76 0.85 0.9504 5-min 0.12 0.13 0.15 0.17 0.19 0.21 0.25 0.31 0.36 0.43 0.49 0.54
05 10-day 1.64 1.97 2.27 2.67 3.07 3.34 4.14 5.28 6.21 7.59 8.75 10.0205 5-day 1.38 1.65 1.86 2.16 2.48 2.70 3.36 4.30 5.07 6.25 7.26 8.3605 72-hr 1.18 1.38 1.56 1.81 2.08 2.26 2.88 3.74 4.46 5.45 6.31 7.2605 48-hr 1.04 1.22 1.36 1.58 1.81 1.97 2.53 3.34 4.01 4.97 5.81 6.7305 24-hr 0.97 1.13 1.24 1.43 1.63 1.77 2.28 3.00 3.60 4.48 5.24 6.0705 18-hr 0.91 1.06 1.16 1.34 1.53 1.66 2.14 2.82 3.38 4.21 4.93 5.7105 12-hr 0.85 0.99 1.08 1.25 1.42 1.54 1.98 2.61 3.13 3.90 4.56 5.2805 6-hr 0.73 0.85 0.93 1.08 1.22 1.33 1.71 2.25 2.70 3.36 3.93 4.5505 3-hr 0.62 0.72 0.79 0.92 1.04 1.13 1.46 1.92 2.30 2.87 3.35 3.8805 2-hr 0.57 0.66 0.72 0.83 0.95 1.03 1.32 1.74 2.09 2.60 3.04 3.5205 1-hr 0.46 0.53 0.58 0.67 0.76 0.83 1.07 1.41 1.69 2.11 2.46 2.8505 30-min 0.36 0.42 0.45 0.53 0.60 0.65 0.84 1.11 1.33 1.66 1.94 2.2505 15-min 0.26 0.31 0.34 0.39 0.44 0.48 0.62 0.81 0.97 1.21 1.41 1.6405 10-min 0.20 0.24 0.26 0.30 0.34 0.37 0.48 0.63 0.76 0.94 1.10 1.2705 5-min 0.12 0.13 0.15 0.17 0.19 0.21 0.27 0.36 0.43 0.54 0.63 0.73
06 10-day 1.76 2.12 2.44 2.87 3.30 3.59 4.31 5.36 6.21 7.46 8.51 9.5406 5-day 1.44 1.72 1.95 2.26 2.59 2.82 3.40 4.22 4.89 6.11 7.17 8.3106 72-hr 1.23 1.45 1.64 1.90 2.18 2.37 2.88 3.62 4.24 5.27 6.17 7.1806 48-hr 1.09 1.28 1.42 1.65 1.90 2.06 2.51 3.17 3.71 4.59 5.35 6.2006 24-hr 1.02 1.19 1.30 1.51 1.71 1.86 2.27 2.85 3.34 4.15 4.84 5.6206 18-hr 0.96 1.12 1.23 1.42 1.61 1.75 2.13 2.68 3.14 3.90 4.55 5.2806 12-hr 0.89 1.04 1.13 1.31 1.49 1.62 1.97 2.48 2.91 3.61 4.21 4.8906 6-hr 0.76 0.89 0.97 1.13 1.28 1.39 1.70 2.14 2.50 3.11 3.63 4.2206 3-hr 0.65 0.76 0.83 0.96 1.09 1.19 1.45 1.82 2.14 2.66 3.10 3.6006 2-hr 0.59 0.69 0.76 0.87 0.99 1.08 1.32 1.65 1.94 2.41 2.81 3.2606 1-hr 0.48 0.56 0.61 0.70 0.80 0.87 1.07 1.34 1.57 1.95 2.27 2.6406 30-min 0.38 0.44 0.48 0.56 0.63 0.69 0.84 1.05 1.24 1.54 1.79 2.0806 15-min 0.28 0.32 0.35 0.41 0.46 0.50 0.61 0.77 0.90 1.12 1.31 1.5206 10-min 0.21 0.25 0.27 0.32 0.36 0.39 0.48 0.60 0.70 0.87 1.02 1.1806 5-min 0.12 0.14 0.15 0.18 0.20 0.22 0.27 0.34 0.40 0.50 0.58 0.67
128
Table 5. Continued
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
07 10-day 1.57 1.89 2.18 2.56 2.94 3.20 3.88 4.75 5.39 6.21 6.83 7.4807 5-day 1.22 1.46 1.66 1.92 2.21 2.40 2.96 3.68 4.23 4.99 5.61 6.2607 72-hr 1.11 1.30 1.47 1.70 1.96 2.13 2.62 3.28 3.78 4.49 5.05 5.6607 48-hr 1.02 1.20 1.33 1.54 1.78 1.93 2.37 2.97 3.41 4.03 4.52 5.0407 24-hr 0.96 1.12 1.23 1.42 1.61 1.75 2.14 2.65 3.05 3.56 3.97 4.4007 18-hr 0.90 1.05 1.15 1.33 1.51 1.64 2.01 2.49 2.87 3.35 3.73 4.1407 12-hr 0.84 0.97 1.06 1.23 1.40 1.52 1.86 2.31 2.65 3.10 3.45 3.8307 6-hr 0.72 0.84 0.92 1.06 1.21 1.31 1.61 1.99 2.29 2.67 2.98 3.3007 3-hr 0.62 0.72 0.78 0.91 1.03 1.12 1.37 1.70 1.95 2.28 2.54 2.8207 2-hr 0.56 0.65 0.71 0.82 0.93 1.01 1.24 1.54 1.77 2.06 2.30 2.5507 1-hr 0.45 0.52 0.57 0.66 0.75 0.82 1.01 1.25 1.43 1.67 1.87 2.0707 30-min 0.36 0.42 0.45 0.53 0.60 0.65 0.79 0.98 1.13 1.32 1.47 1.6307 15-min 0.26 0.30 0.33 0.38 0.43 0.47 0.58 0.72 0.82 0.96 1.07 1.1907 10-min 0.20 0.24 0.26 0.30 0.34 0.37 0.45 0.56 0.64 0.75 0.83 0.9207 5-min 0.12 0.13 0.15 0.17 0.19 0.21 0.26 0.32 0.37 0.43 0.48 0.53
08 10-day 1.81 2.18 2.51 2.95 3.39 3.69 4.33 5.23 5.96 7.39 8.63 10.0308 5-day 1.48 1.77 2.00 2.32 2.67 2.90 3.45 4.27 4.95 6.16 7.28 8.4608 72-hr 1.29 1.52 1.72 1.99 2.29 2.49 3.00 3.75 4.41 5.50 6.45 7.5108 48-hr 1.14 1.33 1.48 1.72 1.98 2.15 2.63 3.32 3.91 4.93 5.83 6.8208 24-hr 1.07 1.25 1.37 1.58 1.79 1.95 2.37 3.00 3.52 4.45 5.27 6.1508 18-hr 1.01 1.17 1.28 1.48 1.68 1.83 2.23 2.82 3.31 4.18 4.95 5.7808 12-hr 0.94 1.09 1.19 1.38 1.56 1.70 2.06 2.61 3.06 3.87 4.58 5.3508 6-hr 0.80 0.93 1.02 1.18 1.34 1.46 1.78 2.25 2.64 3.34 3.95 4.6108 3-hr 0.69 0.80 0.88 1.01 1.15 1.25 1.52 1.92 2.25 2.85 3.37 3.9408 2-hr 0.62 0.72 0.79 0.92 1.04 1.13 1.37 1.74 2.04 2.58 3.06 3.5708 1-hr 0.51 0.59 0.64 0.75 0.85 0.92 1.11 1.41 1.65 2.09 2.48 2.8908 30-min 0.40 0.46 0.50 0.58 0.66 0.72 0.88 1.11 1.30 1.65 1.95 2.2808 15-min 0.29 0.34 0.37 0.43 0.49 0.53 0.64 0.81 0.95 1.20 1.42 1.6608 10-min 0.23 0.26 0.29 0.33 0.38 0.41 0.50 0.63 0.74 0.93 1.11 1.2908 5-min 0.13 0.15 0.16 0.19 0.21 0.23 0.28 0.36 0.42 0.53 0.63 0.74
09 10-day 1.77 2.13 2.45 2.89 3.32 3.61 4.26 5.15 5.83 6.81 7.60 8.4009 5-day 1.43 1.71 1.93 2.24 2.58 2.80 3.36 4.10 4.71 5.57 6.27 6.9909 72-hr 1.27 1.49 1.68 1.95 2.24 2.44 2.93 3.59 4.16 4.95 5.59 6.2809 48-hr 1.17 1.37 1.52 1.77 2.03 2.21 2.66 3.28 3.79 4.50 5.10 5.7309 24-hr 1.12 1.30 1.42 1.64 1.87 2.03 2.42 2.98 3.43 4.09 4.63 5.2009 18-hr 1.05 1.22 1.34 1.55 1.76 1.91 2.27 2.80 3.22 3.84 4.35 4.8909 12-hr 0.97 1.13 1.24 1.43 1.63 1.77 2.11 2.59 2.98 3.56 4.03 4.5209 6-hr 0.84 0.97 1.06 1.23 1.40 1.52 1.82 2.24 2.57 3.07 3.47 3.9009 3-hr 0.71 0.83 0.91 1.05 1.20 1.30 1.55 1.91 2.20 2.62 2.96 3.3309 2-hr 0.65 0.76 0.83 0.96 1.09 1.18 1.40 1.73 1.99 2.37 2.69 3.0209 1-hr 0.52 0.61 0.66 0.77 0.87 0.95 1.14 1.40 1.61 1.92 2.18 2.4409 30-min 0.41 0.48 0.52 0.61 0.69 0.75 0.90 1.10 1.27 1.51 1.71 1.9209 15-min 0.30 0.35 0.38 0.45 0.51 0.55 0.65 0.80 0.93 1.10 1.25 1.4009 10-min 0.24 0.28 0.30 0.35 0.40 0.43 0.51 0.63 0.72 0.86 0.97 1.0909 5-min 0.13 0.15 0.17 0.19 0.22 0.24 0.29 0.36 0.41 0.49 0.56 0.62
129
Table 5. Concluded
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
10 10-day 1.56 1.88 2.17 2.55 2.93 3.19 3.82 4.64 5.27 6.11 6.79 7.5110 5-day 1.28 1.53 1.73 2.01 2.31 2.51 3.05 3.68 4.16 4.78 5.26 5.7410 72-hr 1.18 1.38 1.56 1.81 2.08 2.26 2.74 3.34 3.76 4.31 4.74 5.1610 48-hr 1.08 1.26 1.41 1.63 1.88 2.04 2.48 3.04 3.44 3.96 4.36 4.7810 24-hr 1.03 1.20 1.31 1.51 1.72 1.87 2.26 2.75 3.13 3.60 3.98 4.3610 18-hr 0.97 1.13 1.23 1.43 1.62 1.76 2.12 2.59 2.94 3.38 3.74 4.1010 12-hr 0.90 1.04 1.14 1.32 1.50 1.63 1.97 2.39 2.72 3.13 3.46 3.7910 6-hr 0.77 0.90 0.98 1.13 1.29 1.40 1.69 2.06 2.35 2.70 2.99 3.2710 3-hr 0.66 0.77 0.84 0.97 1.10 1.20 1.45 1.76 2.00 2.30 2.55 2.7910 2-hr 0.59 0.69 0.76 0.87 0.99 1.08 1.31 1.59 1.82 2.09 2.31 2.5310 1-hr 0.48 0.56 0.62 0.71 0.81 0.88 1.06 1.29 1.47 1.69 1.87 2.0510 30-min 0.38 0.44 0.48 0.56 0.63 0.69 0.84 1.02 1.16 1.33 1.47 1.6110 15-min 0.28 0.32 0.35 0.41 0.46 0.50 0.61 0.74 0.85 0.97 1.07 1.1810 10-min 0.21 0.25 0.27 0.32 0.36 0.39 0.47 0.58 0.66 0.76 0.84 0.9210 5-min 0.12 0.14 0.15 0.18 0.20 0.22 0.27 0.33 0.38 0.43 0.48 0.52
130
Table 6. Sectional Mean Frequency Distributions for Storm Periods of 5 Minutes to 10 Daysand Recurrence Intervals of 2 Months to 100 Years in Minnesota
Sectional code (see figure 1 on page 4)
01 - Northwest 06 - East Central02 - North Central 07 - Southwest03 - Northeast 08 - South Central04 - West Central 09 - Southeast05 - Central
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
01 10-day 1.53 1.84 2.12 2.50 2.87 3.12 3.83 4.89 5.80 6.97 7.88 8.7501 5-day 1.27 1.53 1.73 2.00 2.30 2.50 3.11 4.11 5.01 6.12 7.05 7.9401 72-hr 1.11 1.30 1.47 1.70 1.96 2.13 2.70 3.61 4.43 5.55 6.41 7.2701 48-hr 1.03 1.20 1.34 1.55 1.78 1.94 2.42 3.25 4.05 5.13 5.91 6.7001 24-hr 0.94 1.09 1.20 1.39 1.57 1.71 2.16 2.94 3.69 4.57 5.41 6.1101 18-hr 0.89 1.03 1.13 1.30 1.48 1.61 2.03 2.76 3.47 4.30 5.09 5.7401 12-hr 0.82 0.95 1.04 1.21 1.37 1.49 1.88 2.56 3.21 3.98 4.71 5.3201 6-hr 0.70 0.82 0.90 1.04 1.18 1.28 1.62 2.20 2.77 3.43 4.06 4.5801 3-hr 0.60 0.70 0.76 0.88 1.00 1.09 1.38 1.88 2.36 2.92 3.46 3.9101 2-hr 0.54 0.63 0.69 0.80 0.91 0.99 1.25 1.71 2.14 2.65 3.14 3.5401 1-hr 0.44 0.51 0.56 0.65 0.74 0.80 1.02 1.38 1.73 2.15 2.54 2.8701 30-min 0.35 0.40 0.44 0.51 0.58 0.63 0.80 1.09 1.37 1.69 2.00 2.2601 15-mín 0.25 0.29 0.32 0.37 0.42 0.46 0.58 0.79 1.00 1.23 1.46 1.6501 10-min 0.20 0.23 0.25 0.29 0.33 0.36 0.45 0.62 0.77 0.96 1.14 1.2801 5-min 0.12 0.13 0.15 0.17 0.19 0.21 0.26 0.35 0.44 0.55 0.65 0.73
02 10-day 1.67 2.01 2.32 2.73 3.14 3.41 4.15 5.08 5.81 6.84 7.68 8.5202 5-day 1.35 1.61 1.82 2.11 2.43 2.64 3.27 4.14 4.84 5.86 6.71 7.5702 72-hr 1.24 1.45 1.64 1.90 2.19 2.38 2.90 3.64 4.31 5.28 6.10 6.9602 48-hr 1.14 1.33 1.48 1.72 1.98 2.15 2.68 3.38 3.97 4.86 5.62 6.4502 24-hr 1.07 1.24 1.36 1.57 1.78 1.94 2.41 3.06 3.58 4.39 5.10 5.8802 18-hr 1.00 1.16 1.27 1.47 1.67 1.82 2.27 2.88 3.37 4.13 4.79 5.5302 12-hr 0.93 1.08 1.18 1.37 1.55 1.69 2.10 2.66 3.11 3.82 4.44 5.1202 6-hr 0.80 0.93 1.02 1.18 1.34 1.46 1.81 2.30 2.68 3.29 3.82 4.4102 3-hr 0.68 0.79 0.87 1.00 1.14 1.24 1.54 1.96 2.29 2.81 3.26 3.7602 2-hr 0.62 0.72 0.79 0.92 1.04 1.13 1.40 1.77 2.08 2.55 2.96 3.4102 1-hr 0.50 0.58 0.64 0.74 0.84 0.91 1.13 1.44 1.68 2.06 2.40 2.7602 30-min 0.40 0.46 0.50 0.58 0.66 0.72 0.89 1.13 1.32 1.62 1.89 2.1802 15-min 0.29 0.33 0.36 0.42 0.48 0.52 0.65 0.83 0.97 1.19 1.38 1.5902 10-min 0.23 0.26 0.29 0.33 0.38 0.41 0.51 0.64 0.75 0.92 1.07 1.2302 5-min 0.13 0.15 0.16 0.19 0.21 0.23 0.29 0.37 0.43 0.53 0.61 0.71
03 10-day 1.66 1.99 2.30 2.70 3.11 3.38 4.04 4.82 5.41 6.28 6.96 7.5803 5-day 1.36 1.62 1.84 2.13 2.45 2.66 3.24 4.05 4.69 5.54 6.16 6.5703 72-hr 1.19 1.39 1.57 1.82 2.10 2.28 2.83 3.57 4.16 4.96 5.53 6.0903 48-hr 1.09 1.28 1.42 1.65 1.90 2.06 2.54 3.21 3.74 4.49 5.06 5.6303 24-hr 1.05 1.22 1.34 1.55 1.76 1.91 2.31 2.88 3.36 4.08 4.64 5.2003 18-hr 0.99 1.15 1.26 1.46 1.66 1.80 2.17 2.71 3.16 3.84 4.36 4.8903 12-hr 0.91 1.06 1.16 1.34 1.53 1.66 2.01 2.51 2.92 3.55 4.04 4.5203 6-hr 0.79 0.92 1.00 1.16 1.32 1.43 1.73 2.16 2.52 3.06 3.48 3.9003 3-hr 0.67 0.78 0.85 0.99 1.12 1.22 1.48 1.84 2.15 2.61 2.97 3.3303 2-hr 0.61 0.71 0.78 0.90 1.02 1.11 1.34 1.67 1.95 2.37 2.69 3.0203 1-hr 0.50 0.58 0.63 0.73 0.83 0.90 1.09 1.35 1.58 1.92 2.18 2.4403 30-min 0.39 0.45 0.50 0.58 0.65 0.71 0.85 1.07 1.24 1.51 1.72 1.9203 15-min 0.29 0.33 0.36 0.42 0.48 0.52 0.62 0.78 0.91 1.10 1.25 1.4003 10-min 0.22 0.26 0.28 0.32 0.37 0.40 0.49 0.60 0.71 0.86 0.97 1.0903 5-min 0.13 0.15 0.16 0.19 0.21 0.23 0.28 0.35 0.40 0.49 0.56 0.62
131
1.47
Table 6. Continued
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
04 10-day 1.70 2.04 2.35 2.77 3.18 3.46 4.18 5.21 6.08 7.25 8.17 9.0704 5-day 1.45 1.73 1.96 2.27 2.61 2.84 3.38 4.20 4.92 6.03 7.05 8.2004 72-hr 1.27 1.49 1.69 1.96 2.25 2.45 2.93 3.62 4.26 5.22 6.11 7.0604 48-hr 1.18 1.38 1.53 1.78 2.04 2.22 2.65 3.28 3.83 4.64 5.38 6.2304 24-hr 1.12 1.30 1.42 1.64 1.87 2.03 2.40 2.95 3.42 4.19 4.83 5.5704 18-hr 1.05 1.22 1.34 1.55 1.76 1.91 2.26 2.77 3.21 3.94 4.54 5.2404 12-hr 0.97 1.13 1.24 1.43 1.63 1.77 2.09 2.57 2.98 3.65 4.20 4.8504 6-hr 0.84 0.97 1.06 1.23 1.40 1.52 1.80 2.21 2.57 3.14 3.62 4.1804 3-hr 0.71 0.83 0.91 1.05 1.20 1.30 1.54 1.89 2.19 2.68 3.09 3.5604 2-hr 0.65 0.76 0.83 0.96 1.09 1.18 1.39 1.71 1.98 2.43 2.80 3.2304 1-hr 0.52 0.61 0.66 0.77 0.87 0.95 1.13 1.39 1.61 1.97 2.27 2.6204 30-min 0.41 0.48 0.52 0.61 0.69 0.75 0.89 1.09 1.27 1.55 1.79 2.0604 15-min 0.30 0.35 0.38 0.45 0.51 0.55 0.65 0.80 0.92 1.13 1.30 1.5004 10-min 0.24 0.28 0.30 0.35 0.40 0.43 0.50 0.62 0.72 0.88 1.01 1.1704 5-min 0.13 0.15 0.17 0.19 0.22 0.24 0.29 0.35 0.41 0.50 0.58 0.67
05 10-day 1.76 2.12 2.44 2.87 3.30 3.59 4.19 5.43 6.24 7.34 8.25 9.2305 5-day 1.45 1.74 1.97 2.28 2.62 2.85 3.51 4.43 5.18 6.21 7.09 8.0205 72-hr 1.31 1.53 1.73 2.01 2.31 2.51 3.05 3.81 4.45 5.40 6.22 7.1005 48-hr 1.22 1.43 1.59 1.84 2.12 2.30 2.78 3.48 4.05 4.88 5.59 6.3705 24-hr 1.15 1.34 1.47 1.70 1.93 2.10 2.54 3.17 3.68 4.43 5.03 5.7205 18-hr 1.08 1.26 1.38 1.60 1.81 1.97 2.39 2.98 3.46 4.16 4.73 5.3805 12-hr 1.01 1.17 1.28 1.48 1.68 1.83 2.21 2.76 3.20 3.85 4.38 4.9805 6-hr 0.86 1.00 1.10 1.27 1.44 1.57 1.90 2.38 2.76 3.32 3.77 4.2905 3-hr 0.74 0.86 0.94 1.09 1.23 1.34 1.63 2.03 2.36 2.84 3.22 3.6605 2-hr 0.67 0.78 0.85 0.99 1.12 1.22 1.47 1.84 2.13 2.57 2.92 3.3205 1-hr 0.54 0.63 0.69 0.80 0.91 0.99 1.19 1.49 1.73 2.08 2.36 2.6905 30-min 0.43 0.50 0.55 0.63 0.72 0.78 0.94 1.17 1.36 1.64 1.86 2.1205 15-min 0.31 0.36 0.40 0.46 0.52 0.57 0.69 0.86 0.99 1.20 1.36 1.5405 10-min 0.24 0.28 0.31 0.36 0.40 0.44 0.53 0.67 0.77 0.93 1.06 1.2005 5-min 0.14 0.16 0.17 0.20 0.23 0.25 0.30 0.38 0.44 0.53 0.60 0.69
06 10-day 1.83 2.21 2.54 2.99 3.44 3.74 4.53 5.51 6.23 7.16 7.90 8.6806 5-day 1.55 1.85 2.09 2.42 2.79 3.03 3.66 4.50 5.15 6.11 6.86 7.6906 72-hr 1.37 1.61 1.82 2.11 2.43 2.64 3.16 3.85 4.41 5.19 5.85 6.5906 48-hr 1.28 1.50 1.67 1.94 2.23 2.42 2.89 3.53 4.03 4.74 5.36 6.0206 24-hr 1.22 1.42 1.55 1.80 2.04 2.22 2.65 3.23 3.69 4.35 4.88 5.4606 18-hr 1.15 1.34 1.46 1.69 1.92 2.09 2.49 3.04 3.47 4.09 4.59 5.1306 12-hr 1.06 1.24 1.35 1.56 1.78 1.93 2.31 2.81 3.21 3.78 4.25 4.7506 6-hr 0.91 1.06 1.16 1.34 1.53 1.66 1.99 2.42 2.77 3.26 3.66 4.1006 3-hr 0.78 0.91 0.99 1.15 1.31 1.42 1.70 2.07 2.36 2.78 3.12 3.4906 2-hr 0.71 0.83 0.90 1.04 1.19 1.29 1.54 1.87 2.14 2.52 2.83 3.1706 1-hr 0.57 0.67 0.73 0.84 0.96 1.04 1.25 1.52 1.73 2.04 2.29 2.5706 30-min 0.45 0.52 0.57 0.66 0.75 0.82 0.98 1.20 1.37 1.61 1.81 2.0206 15-min 0.33 0.38 0.42 0.49 0.55 0.60 0.72 0.87 1.00 1.17 1.3206 10-min 0.26 0.30 0.33 0.38 0.43 0.47 0.56 0.68 0.77 0.91 1.02 1.1506 5-min 0.15 0.17 0.19 0.22 0.25 0.27 0.32 0.39 0.44 0.52 0.59 0.66
132
Table 6. Continued
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
07 10-day 1.88 2.27 2.61 3.07 3.53 3.84 4.51 5.45 6.16 7.25 8.20 9.1307 5-day 1.59 1.90 2.15 2.49 2.86 3.11 3.72 4.53 5.18 6.17 7.03 8.0207 72-hr 1.44 1.68 1.90 2.21 2.54 2.76 3.24 3.96 4.57 5.50 5.93 7.1307 48-hr 1.30 1.52 1.69 1.96 2.25 2.45 2.92 3.60 4.18 5.04 5.74 6.4807 24-hr 1.24 1.45 1.58 1.83 2.08 2.26 2.69 3.32 3.81 4.55 5.20 5.9407 18-hr 1.17 1.36 1.48 1.72 1.95 2.12 2.53 3.12 3.58 4.28 4.89 5.5807 12-hr 1.08 1.26 1.38 1.60 1.81 1.97 2.34 2.89 3.31 3.96 4.52 5.1707 6-hr 0.93 1.08 1.18 1.37 1.55 1.69 2.02 2.49 2.86 3.41 3.90 4.4507 3-hr 0.80 0.93 1.01 1.17 1.33 1.45 1.72 2.12 2.44 2.91 3.33 3.8007 2-hr 0.72 0.84 0.92 1.06 1.21 1.31 1.56 1.93 2.21 2.64 3.02 3.4507 1-hr 0.58 0.68 0.74 0.86 0.98 1.06 1.26 1.56 1.79 2.14 2.44 2.7907 30-min 0.46 0.54 0.59 0.68 0.77 0.84 1.00 1.23 1.41 1.68 1.92 2.2007 15-min 0.34 0.39 0.43 0.49 0.56 0.61 0.73 0.90 1.03 1.23 1.40 1.6007 10-min 0.26 0.30 0.33 0.38 0.43 0.47 0.56 0.70 0.80 0.96 1.09 1.2507 5-min 0.15 0.17 0.19 0.22 0.25 0.27 0.32 0.40 0.46 0.55 0.62 0.71
08 10-day 1.86 2.24 2.58 3.04 3.50 3.80 4.59 5.67 6.52 7.60 8.47 9.1608 5-day 1.55 1.85 2.09 2.42 2.79 3.03 3.71 4.66 5.43 6.38 7.72 8.4308 72-hr 1.37 1.60 1.81 2.10 2.42 2.63 3.22 4.06 4.77 5.67 6.43 7.0808 48-hr 1.27 1.49 1.66 1.92 2.21 2.40 2.93 3.68 4.30 5.14 5.82 6.3808 24-hr 1.20 1.40 1.53 1.77 2.01 2.19 2.68 3.38 3.95 4.66 5.28 5.8508 18-hr 1.13 1.32 1.44 1.67 1.90 2.06 2.52 3.18 3.71 4.38 4.96 5.5008 12-hr 1.05 1.22 1.34 1.55 1.76 1.91 2.33 2.94 3.44 4.05 4.59 5.0908 6-hr 0.90 1.05 1.15 1.33 1.51 1.64 2.01 2.54 2.96 3.49 3.96 4.3908 3-hr 0.77 0.90 0.98 1.13 1.29 1.40 1.72 2.16 2.53 2.98 3.38 3.7408 2-hr 0.70 0.81 0.89 1.03 1.17 1.27 1.55 1.96 2.29 2.70 3.06 3.3908 1-hr 0.57 0.66 0.72 0.83 0.95 1.03 1.26 1.59 1.86 2.19 2.48 2.7508 30-min 0.45 0.52 0.57 0.66 0.75 0.81 0.99 1.25 1.46 1.72 1.95 2.1608 15-min 0.32 0.38 0.41 0.48 0.54 0.59 0.72 0.91 1.07 1.26 1.43 1.5808 10-min 0.25 0.29 0.32 0.37 0.42 0.46 0.56 0.71 0.83 0.98 1.11 1.2308 5-min 0.14 0.17 0.18 0.21 0.24 0.26 0.32 0.41 0.47 0.56 0.63 0.70
09 10-day 1.89 2.28 2.62 3.09 3.55 3.86 4.81 5.93 6.72 7.70 8.42 9.1009 5-day 1.63 1.95 2.20 2.55 2.93 3.19 3.95 4.89 5.55 6.38 7.01 7.6309 72-hr 1.42 1.67 1.88 2.18 2.51 2.73 3.48 4.35 4.97 5.74 6.30 6.8309 48-hr 1.33 1.56 1.73 2.01 2.31 2.51 3.15 3.94 4.52 5.24 5.81 6.4309 24-hr 1.24 1.45 1.58 1.83 2.08 2.26 2.84 3.55 4.08 4.75 5.25 5.7609 18-hr 1.17 1.36 1.48 1.72 1.95 2.12 2.67 3.34 3.84 4.47 4.93 5.4109 12-hr 1.08 1.26 1.38 1.60 1.81 1.97 2.47 3.09 3.55 4.13 4.57 5.0109 6-hr 0.93 1.08 1.18 1.37 1.55 1.69 2.13 2.66 3.06 3.56 3.94 4.3209 3-hr 0.80 0.93 1.01 1.17 1.33 1.45 1.82 2.27 2.61 3.04 3.36 3.6909 2-hr 0.72 0.84 0.92 1.06 1.21 1.31 1.65 2.06 2.37 2.75 3.04 3.3409 1-hr 0.58 0.68 0.74 0.86 0.98 1.06 1.33 1.67 1.92 2.23 2.47 2.7109 30-min 0.46 0.54 0.59 0.68 0.77 0.84 1.05 1.31 1.51 1.76 1.94 2.1309 15-min 0.34 0.39 0.43 0.49 0.56 0.61 0.77 0.96 1.10 1.28 1.42 1.5609 10-min 0.26 0.30 0.33 0.38 0.43 0.47 0.60 0.75 0.86 1.00 1.10 1.2109 5-min 0.15 0.17 0.19 0.22 0.25 0.27 0.34 0.43 0.49 0.57 0.63 0.69
133
Table 7. Sectional Mean Frequency Distributions for Storm Periods of 5 Minutes to 10 Daysand Recurrence Intervals of 2 Months to 100 Years in Missouri
Sectional code (see figure 1 on page 4)
01 - Northwest Prairie 04 - West Ozarks02 - Northeast Prairie 05 - East Ozarks03 - West Central Plains 06 - Bootheel
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
01 10-day 2.18 2.62 3.02 3.55 4.08 4.44 5.60 7.01 8.01 9.27 10.20 11.2501 5-day 1.82 2.17 2.46 2.85 3.28 3.56 4.50 5.69 6.60 7.78 8.71 9.7101 72-hr 1.62 1.90 2.15 2.50 2.87 3.12 3.99 5.11 5.98 7.07 7.92 8.8201 48-hr 1.48 1.73 1.93 2.23 2.57 2.79 3.59 4.63 5.43 6.43 7.17 7.9901 24-hr 1.39 1.62 1.77 2.05 2.33 2.53 3.27 4.25 4.98 5.89 6.58 7.3001 18-hr 1.31 1.52 1.67 1.93 2.19 2.38 3.07 3.99 4.68 5.54 6.19 6.8601 12-hr 1.21 1.41 1.54 1.78 2.02 2.20 2.84 3.70 4.33 5.12 5.72 6.3501 6-hr 1.04 1.22 1.33 1.54 1.75 1.90 2.45 3.19 3.74 4.42 4.93 5.4801 3-hr 0.89 1.04 1.13 1.31 1.49 1.62 2.09 2.72 3.19 3.77 4.21 4.6701 2-hr 0.81 0.94 1.03 1.19 1.35 1.47 1.90 2.46 2.89 3.42 3.82 4.2301 1-hr 0.65 0.76 0.83 0.96 1.09 1.19 1.54 2.00 2.34 2.77 3.09 3.4301 30-min 0.52 0.60 0.66 0.76 0.86 0.94 1.21 1.57 1.84 2.18 2.43 2.7001 15-min 0.37 0.44 0.48 0.55 0.63 0.68 0.88 1.15 1.34 1.59 1.78 1.9701 10-min 0.29 0.34 0.37 0.43 0.49 0.53 0.69 0.89 1.05 1.24 1.38 1.5301 5-min 0.17 0.19 0.21 0.24 0.28 0.30 0.39 0.51 0.60 0.71 0.79 0.88
02 10-day 2.21 2.66 3.07 3.61 4.15 4.51 5.41 6.64 7.62 8.90 9.92 11.0202 5-day 1.79 2.14 2.42 2.81 3.23 3.51 4.27 5.37 6.27 7.53 8.51 9.5702 72-hr 1.63 1.91 2.16 2.50 2.88 3.13 3.82 4.81 5.66 6.81 7.74 8.7602 48-hr 1.48 1.74 1.93 2.24 2.58 2.80 3.44 4.33 5.09 6.14 6.99 7.9102 24-hr 1.38 1.60 1.75 2.03 2.30 2.50 3.10 3.94 4.64 5.60 6.38 7.2102 18-hr 1.29 1.50 1.64 1.90 2.16 2.35 2.91 3.70 4.36 5.26 6.00 6.7802 12-hr 1.19 1.39 1.52 1.76 2.00 2.17 2.70 3.43 4.04 4.87 5.55 6.2702 6-hr 1.03 1.20 1.32 1.52 1.73 1.88 2.32 2.95 3.48 4.20 4.78 5.4102 3-hr 0.88 1.02 1.12 1.30 1.47 1.60 1.98 2.52 2.97 3.58 4.08 4.6102 2-hr 0.80 0.93 1.01 1.17 1.33 1.45 1.80 2.29 2.69 3.25 3.70 4.1802 1-hr 0.64 0.75 0.82 0.95 1.08 1.17 1.46 1.85 2.18 2.63 3.00 3.3902 30-min 0.51 0.60 0.65 0.75 0.86 0.93 1.15 1.46 1.72 2.07 2.36 2.6702 15-min 0.37 0.44 0.48 0.55 0.63 0.68 0.84 1.06 1.25 1.51 1.72 1.9502 10-min 0.29 0.33 0.36 0.42 0.48 0.52 0.65 0.83 0.97 1.18 1.34 1.5102 5-min 0.17 0.19 0.21 0.24 0.28 0.30 0.37 0.47 0.56 0.67 0.77 0.87
03 10-day 2.38 2.87 3.30 3.89 4.47 4.86 6.10 7.59 8.62 9.88 10.87 11.7203 5-day 2.04 2.44 2.76 3.20 3.68 4.00 4.92 6.12 7.06 8.33 9.31 10.3603 72-hr 1.79 2.10 2.38 2.76 3.17 3.45 4.25 5.33 6.20 7.39 8.32 9.3003 48-hr 1.66 1.94 2.16 2.50 2.88 3.13 3.90 4.92 5.71 6.78 7.66 8.5703 24-hr 1.55 1.80 1.97 2.28 2.59 2.81 3.50 4.41 5.16 6.16 6.93 7.7403 18-hr 1.45 1.69 1.85 2.14 2.43 2.64 3.29 4.15 4.85 5.79 6.51 7.2803 12-hr 1.34 1.56 1.71 1.98 2.24 2.44 3.05 3.84 4.49 5.36 6.03 6.7303 6-hr 1.16 1.35 1.48 1.71 1.94 2.11 2.62 3.31 3.87 4.62 5.20 5.8003 3-hr 0.99 1.15 1.26 1.46 1.66 1.80 2.24 2.82 3.30 3.94 4.44 4.9503 2-hr 0.90 1.04 1.14 1.32 1.50 1.63 2.03 2.56 2.99 3.57 4.02 4.4903 1-hr 0.73 0.84 0.92 1.07 1.21 1.32 1.64 2.07 2.43 2.90 3.26 3.6403 30-min 0.57 0.67 0.73 0.84 0.96 1.04 1.30 1.63 1.91 2.28 2.56 2.8603 15-min 0.42 0.49 0.53 0.62 0.70 0.76 0.95 1.19 1.39 1.66 1.87 2.0903 10-min 0.32 0.38 0.41 0.48 0.54 0.59 0.73 0.93 1.08 1.29 1.46 1.6303 5-min 0.19 0.22 0.24 0.28 0.31 0.34 0.42 0.53 0.62 0.74 0.83 0.93
134
Table 7. Concluded
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
04 10-day 2.63 3.17 3.65 4.30 4.94 5.37 6.59 8.05 9.13 10.49 11.52 12.6104 5-day 2.12 2.54 2.87 3.33 3.83 4.16 5.21 6.50 7.45 8.70 9.68 10.7704 72-hr 1.91 2.24 2.54 2.94 3.39 3.68 4.62 5.81 6.69 7.90 8.85 9.8504 48-hr 1.75 2.05 2.28 2.64 3.04 3.30 4.14 5.25 6.07 7.17 8.05 8.9704 24-hr 1.65 1.92 2.10 2.43 2.76 3.00 3.77 4.79 5.55 6.56 7.34 8.1804 18-hr 1.55 1.80 1.97 2.28 2.59 2.82 3.54 4.50 5.22 6.17 6.90 7.6904 12-hr 1.44 1.67 1.83 2.11 2.40 2.61 3.28 4.17 4.83 5.71 6.39 7.1204 6-hr 1.24 1.44 1.57 1.82 2.07 2.25 2.83 3.59 4.16 4.92 5.51 6.1404 3-hr 1.06 1.23 1.34 1.56 1.77 1.92 2.41 3.07 3.55 4.20 4.70 5.2404 2-hr 0.96 1.11 1.22 1.41 1.60 1.74 2.19 2.78 3.22 3.80 4.26 4.7404 1-hr 0.78 0.90 0.99 1.14 1.30 1.41 1.77 2.25 2.61 3.08 3.45 3.8404 30-min 0.61 0.71 0.78 0.90 1.02 1.11 1.39 1.77 2.05 2.43 2.72 3.0304 15-min 0.45 0.52 0.57 0.66 0.75 0.81 1.02 1.29 1.50 1.77 1.98 2.2104 10-min 0.35 0.40 0.44 0.51 0.58 0.63 0.79 1.01 1.17 1.38 1.54 1.7204 5-min 0.20 0.23 0.25 0.29 0.33 0.36 0.45 0.57 0.67 0.79 0.88 0.98
05 10-day 2.30 2.77 3.20 3.76 4.32 4.70 5.96 7.36 8.29 9.48 10.34 11.3105 5-day 1.92 2.30 2.60 3.02 3.47 3.77 4.78 5.99 6.86 8.02 8.97 9.9305 72-hr 1.75 2.05 2.32 2.69 3.09 3.36 4.24 5.31 6.10 7.15 7.99 8.9005 48-hr 1.61 1.88 2.09 2.42 2.79 3.03 3.82 4.78 5.50 6.47 7.24 8.0605 24-hr 1.53 1.79 1.95 2.26 2.57 2.79 3.51 4.39 5.03 5.94 6.64 7.4205 18-hr 1.44 1.68 1.83 2.12 2.41 2.62 3.30 4.13 4.73 5.58 6.24 6.9705 12-hr 1.34 1.56 1.70 1.97 2.24 2.43 3.05 3.82 4.38 5.17 5.78 6.4605 6-hr 1.15 1.34 1.46 1.69 1.92 2.09 2.63 3.29 3.77 4.45 4.98 5.5705 3-hr 0.98 1.15 1.25 1.45 1.65 1.79 2.25 2.81 3.22 3.80 4.25 4.7505 2-hr 0.89 1.04 1.13 1.31 1.49 1.62 2.04 2.55 2.92 3.45 3.85 4.3005 1-hr 0.72 0.84 0.92 1.06 1.21 1.31 1.65 2.06 2.36 2.79 3.12 3.4905 30-min 0.57 0.66 0.72 0.83 0.95 1.03 1.30 1.62 1.86 2.20 2.46 2.7505 15-min 0.41 0.48 0.52 0.61 0.69 0.75 0.95 1.19 1.36 1.60 1.79 2.0005 10-min 0.32 0.38 0.41 0.48 0.54 0.59 0.74 0.92 1.06 1.25 1.39 1.5605 5-min 0.18 0.21 0.23 0.27 0.30 0.33 0.42 0.53 0.60 0.71 0.80 0.89
06 10-day 2.45 2.94 3.39 3.99 4.59 4.99 6.43 7.99 9.01 10.25 11.15 12.0706 5-day 2.09 2.50 2.83 3.28 3.77 4.10 5.19 6.46 7.31 8.39 9.20 10.0406 72-hr 1.91 2.24 2.53 2.94 3.38 3.67 4.67 5.81 6.60 7.58 8.35 9.1206 48-hr 1.74 2.03 2.26 2.62 3.02 3.28 4.14 5.13 5.84 6.75 7.47 8.2106 24-hr 1.64 1.91 2.09 2.42 2.75 2.99 3.74 4.65 5.29 6.16 6.83 7.5106 18-hr 1.55 1.80 1.97 2.28 2.59 2.81 3.52 4.37 4.97 5.79 6.42 7.0606 12-hr 1.43 1.66 1.82 2.11 2.39 2.60 3.25 4.05 4.60 5.36 5.94 6.5306 6-hr 1.23 1.43 1.57 1.81 2.06 2.24 2.81 3.49 3.97 4.62 5.12 5.6306 3-hr 1.05 1.22 1.34 1.55 1.76 1.91 2.39 2.98 3.39 3.94 4.37 4.8106 2-hr 0.95 1.11 1.21 1.40 1.59 1.73 2.17 2.70 3.07 3.57 3.96 4.3606 1-hr 0.78 0.90 0.99 1.14 1.30 1.41 1.76 2.19 2.49 2.90 3.21 3.5306 30-min 0.61 0.71 0.78 0.90 1.02 1.11 1.38 1.72 1.96 2.28 2.53 2.7806 15-min 0.45 0.52 0.57 0.66 0.75 0.81 1.01 1.26 1.43 1.66 1.84 2.0306 10-min 0.35 0.40 0.44 0.51 0.58 0.63 0.79 0.98 1.11 1.29 1.43 1.5806 5-min 0.20 0.23 0.25 0.29 0.33 0.36 0.45 0.56 0.63 0.74 0.82 0.90
135
Table 8. Sectional Mean Frequency Distributions for Storm Periods of 5 Minutes to 10 Days
and Recurrence Intervals of 2 Months to 100 Years in Ohio
Sectional code (see figure 1 on page 4)
01 – Northwest 06 -Central Hills02 - North Central 07 - Northeast Hills03 – Northeast 08 - Southwest04 - West Central 09 - South Central05 – Central 10 - Southeast
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
01 10-day 1.69 2.04 2.35 2.76 3.17 3.45 4.22 5.17 5.89 6.83 7.56 8.3101 5-day 1.42 1.70 1.93 2.23 2.57 2.79 3.43 4.29 4.92 5.81 6.51 7.2601 72-hr 1.27 1.49 1.69 1.96 2.25 2.45 3.05 3.77 4.33 5.17 5.89 6.7101 48-hr 1.17 1.36 1.52 1.76 2.02 2.20 2.74 3.43 3.96 4.74 5.40 6.1401 24-hr 1.12 1.30 1.42 1.64 1.87 2.03 2.52 3.18 3.70 4.43 5.05 5.7301 18-hr 1.05 1.22 1.34 1.55 1.76 1.91 2.37 2.99 3.48 4.16 4.75 5.3901 12-hr 0.97 1.13 1.24 1.43 1.63 1.77 2.19 2.77 3.22 3.85 4.39 4.9901 6-hr 0.84 0.97 1.06 1.23 1.40 1.52 1.89 2.38 2.78 3.32 3.79 4.3001 3-hr 0.71 0.83 0.91 1.05 1.20 1.30 1.61 2.04 2.37 2.84 3.23 3.6701 2-hr 0.65 0.76 0.83 0.96 1.09 1.18 1.46 1.84 2.15 2.57 2.93 3.3201 1-hr 0.52 0.61 0.66 0.77 0.87 0.95 1.18 1.49 1.74 2.08 2.37 2.6901 30-min 0.41 0.48 0.52 0.61 0.69 0.75 0.93 1.18 1.37 1.64 1.87 2.1201 15-min 0.30 0.35 0.38 0.45 0.51 0.55 0.68 0.86 1.00 1.20 1.36 1.5501 10-min 0.24 0.28 0.30 0.35 0.40 0.43 0.53 0.67 0.78 0.93 1.06 1.2001 5-min 0.13 0.15 0.17 0.19 0.22 0.24 0.30 0.38 0.44 0.53 0.61 0.69
02 10-day 1.63 1.96 2.26 2.66 3.05 3.32 4.19 5.31 6.19 7.40 8.35 9.3502 5-day 1.35 1.61 1.82 2.11 2.43 2.64 3.33 4.32 5.10 6.21 7.14 8.1402 72-hr 1.22 1.43 1.61 1.87 2.15 2.34 2.93 3.69 4.34 5.39 6.33 7.3902 48-hr 1.14 1.33 1.48 1.72 1.98 2.15 2.67 3.37 3.94 4.86 5.70 6.6802 24-hr 1.09 1.27 1.39 1.60 1.82 1.98 2.44 3.06 3.55 4.35 5.08 5.9202 18-hr 1.02 1.19 1.30 1.51 1.71 1.86 2.29 2.88 3.34 4.09 4.78 5.5602 12-hr 0.95 1.10 1.20 1.39 1.58 1.72 2.12 2.66 3.09 3.78 4.42 5.1502 6-hr 0.82 0.95 1.04 1.21 1.37 1.49 1.83 2.30 2.66 3.26 3.81 4.4402 3-hr 0.70 0.81 0.89 1.03 1.17 1.27 1.56 1.96 2.27 2.78 3.25 3.7902 2-hr 0.63 0.74 0.80 0.93 1.06 1.15 1.42 1.77 2.06 2.52 2.95 3.4302 1-hr 0.51 0.60 0.65 0.75 0.86 0.93 1.15 1.44 1.67 2.04 2.39 2.7802 30-min 0.40 0.47 0.51 0.59 0.67 0.73 0.90 1.13 1.31 1.61 1.88 2.1902 15-min 0.29 0.34 0.37 0.43 0.49 0.53 0.66 0.83 0.96 1.17 1.37 1.6002 10-min 0.23 0.27 0.29 0.34 0.39 0.42 0.51 0.64 0.75 0.91 1.07 1.2402 5-min 0.13 0.15 0.17 0.19 0.22 0.24 0.29 0.37 0.43 0.52 0.61 0.71
03 10-day 1.70 2.05 2.36 2.78 3.19 3.47 4.29 5.34 6.17 7.30 8.19 9.1403 5-day 1.37 1.64 1.86 2.15 2.47 2.69 3.34 4.23 4.95 5.96 6.82 7.7403 72-hr 1.26 1.48 1.67 1.94 2.23 2.42 2.99 3.72 4.34 5.31 6.15 7.0903 48-hr 1.18 1.38 1.53 1.78 2.04 2.22 2.75 3.42 3.99 4.87 5.66 6.5503 24-hr 1.12 1.31 1.43 1.65 1.88 2.04 2.50 3.10 3.60 4.39 5.11 5.8903 18-hr 1.06 1.23 1.34 1.56 1.77 1.92 2.35 2.91 3.38 4.13 4.80 5.5403 12-hr 0.97 1.13 1.24 1.43 1.63 1.77 2.17 2.70 3.13 3.82 4.45 5.1203 6-hr 0.84 0.98 1.07 1.24 1.41 1.53 1.88 2.32 2.70 3.29 3.83 4.4203 3-hr 0.72 0.84 0.92 1.06 1.21 1.31 1.60 1.98 2.30 2.81 3.27 3.7703 2-hr 0.65 0.76 0.83 0.96 1.09 1.18 1.45 1.80 2.09 2.55 2.96 3.4203 1-hr 0.53 0.61 0.67 0.78 0.88 0.96 1.17 1.46 1.69 2.06 2.40 2.7703 30-min 0.41 0.48 0.52 0.61 0.69 0.75 0.93 1.15 1.33 1.62 1.89 2.1803 15-min 0.30 0.35 0.38 0.45 0.51 0.55 0.68 0.84 0.97 1.19 1.38 1.5903 10-min 0.24 0.28 0.30 0.35 0.40 0.43 0.52 0.65 0.76 0.92 1.07 1.2403 5-min 0.13 0.15 0.17 0.19 0.22 0.24 0.30 0.37 0.43 0.53 0.61 0.71
136
Table 8. Continued
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
04 10-day 1.85 2.23 2.57 3.02 3.48 3.78 4.59 5.63 6.43 7.48 8.30 9.1904 5-day 1.54 1.84 2.08 2.41 2.77 3.01 3.65 4.54 5.22 6.17 6.92 7.7404 72-hr 1.36 1.59 1.80 2.09 2.40 2.61 3.19 3.88 4.46 5.33 6.12 6.9704 48-hr 1.25 1.46 1.62 1.88 2.16 2.35 2.91 3.58 4.09 4.88 5.56 6.3504 24-hr 1.18 1.38 1.50 1.74 1.98 2.15 2.69 3.34 3.80 4.46 5.06 5.7004 18-hr 1.11 1.29 1.41 1.64 1.86 2.02 2.53 3.14 3.57 4.19 4.76 5.3604 12-hr 1.03 1.20 1.31 1.51 1.72 1.87 2.34 2.91 3.31 3.88 4.40 4.9604 6-hr 0.89 1.03 1.13 1.30 1.48 1.61 2.02 2.50 2.85 3.35 3.80 4.2704 3-hr 0.76 0.88 0.97 1.12 1.27 1.38 1.72 2.14 2.43 2.85 3.24 3.6504 2-hr 0.69 0.80 0.88 1.01 1.15 1.25 1.56 1.94 2.20 2.59 2.93 3.3104 1-hr 0.56 0.65 0.71 0.82 0.93 1.01 1.26 1.57 1.79 2.10 2.38 2.6804 30-min 0.44 0.51 0.56 0.65 0.74 0.80 1.00 1.24 1.41 1.65 1.87 2.1104 15-min 0.32 0.37 0.41 0.47 0.53 0.58 0.73 0.90 1.03 1.20 1.37 1.5404 10-min 0.25 0.29 0.31 0.36 0.41 0.45 0.56 0.70 0.80 0.94 1.06 1.2004 5-min 0.14 0.17 0.18 0.21 0.24 0.26 0.32 0.40 0.46 0.54 0.61 0.68
05 10-day 1.81 2.18 2.51 2.95 3.39 3.69 4.69 5.93 6.78 7.82 8.56 9.2705 5-day 1.49 1.78 2.01 2.34 2.69 2.92 3.67 4.65 5.39 6.37 7.11 7.8905 72-hr 1.36 1.59 1.80 2.09 2.40 2.61 3.23 3.99 4.54 5.36 6.09 6.9205 48-hr 1.27 1.48 1.65 1.91 2.20 2.39 2.97 3.67 4.21 5.02 5.72 6.5005 24-hr 1.19 1.39 1.52 1.76 2.00 2.17 2.70 3.35 3.86 4.64 5.33 6.0605 18-hr 1.12 1.31 1.43 1.65 1.88 2.04 2.54 3.15 3.63 4.36 5.01 5.7005 12-hr 1.04 1.21 1.32 1.53 1.74 1.89 2.35 2.91 3.36 4.04 4.64 5.2705 6-hr 0.90 1.04 1.14 1.32 1.50 1.63 2.03 2.51 2.89 3.48 4.00 4.5505 3-hr 0.76 0.89 0.97 1.13 1.28 1.39 1.73 2.14 2.47 2.97 3.41 3.8805 2-hr 0.69 0.81 0.88 1.02 1.16 1.26 1.57 1.94 2.24 2.69 3.09 3.5105 1-hr 0.56 0.65 0.71 0.83 0.94 1.02 1.27 1.57 1.81 2.18 2.51 2.8505 30-min 0.44 0.51 0.56 0.65 0.74 0.80 1.00 1.24 1.43 1.72 1.97 2.2405 15-min 0.32 0.38 0.41 0.48 0.54 0.59 0.73 0.90 1.04 1.25 1.44 1.6405 10-min 0.25 0.29 0.32 0.37 0.42 0.46 0.57 0.70 0.81 0.97 1.12 1.2705 5-min 0.14 0.17 0.18 0.21 0.24 0.26 0.32 0.40 0.46 0.56 0.64 0.73
06 10-day 1.72 2.08 2.39 2.82 3.24 3.52 4.35 5.47 6.38 7.61 8.66 9.7406 5-day 1.41 1.68 1.90 2.21 2.54 2.76 3.33 4.24 4.98 6.15 7.12 8.2106 72-hr 1.30 1.53 1.73 2.00 2.30 2.50 2.99 3.72 4.41 5.53 6.54 7.6906 48-hr 1.22 1.43 1.59 1.85 2.13 2.31 2.78 3.44 4.09 5.12 6.06 7.1706 24-hr 1.16 1.35 1.48 1.71 1.94 2.11 2.51 3.11 3.68 4.57 5.41 6.3906 18-hr 1.09 1.27 1.39 1.60 1.82 1.98 2.36 2.92 3.46 4.30 5.09 6.0106 12-hr 1.01 1.18 1.29 1.49 1.69 1.84 2.18 2.71 3.20 3.98 4.71 5.5606 6-hr 0.87 1.01 1.11 1.28 1.45 1.58 1.88 2.33 2.76 3.43 4.06 4.7906 3-hr 0.74 0.86 0.94 1.09 1.24 1.35 1.61 1.99 2.36 2.92 3.46 4.0906 2-hr 0.67 0.78 0.85 0.99 1.12 1.22 1.46 1.80 2.13 2.65 3.14 3.7106 1-hr 0.54 0.63 0.69 0.80 0.91 0.99 1.18 1.46 1.73 2.15 2.54 3.0006 30-min 0.43 0.50 0.55 0.63 0.72 0.78 0.93 1.15 1.36 1.69 2.00 2.3606 15-min 0.31 0.36 0.40 0.46 0.52 0.57 0.68 0.84 0.99 1.23 1.46 1.7306 10-min 0.24 0.28 0.31 0.36 0.40 0.44 0.53 0.65 0.77 0.96 1.14 1.3406 5-min 0.14 0.16 0.17 0.20 0.23 0.25 0.30 0.37 0.44 0.55 0.65 0.77
137
Table 8. Continued
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
07 10-day 1.71 2.06 2.37 2.79 3.21 3.49 4.33 5.37 6.10 7.03 7.77 8.4807 5-day 1.40 1.67 1.89 2.19 2.52 2.74 3.32 4.11 4.72 5.55 6.26 6.9907 72-hr 1.28 1.51 1.70 1.98 2.27 2.47 2.98 3.64 4.15 4.94 5.63 6.3907 48-hr 1.19 1.39 1.55 1.79 2.06 2.24 2.73 3.33 3.81 4.53 5.15 5.8107 24-hr 1.12 1.30 1.42 1.64 1.87 2.03 2.50 3.02 3.42 3.94 4.41 4.9207 18-hr 1.05 1.22 1.34 1.55 1.76 1.91 2.35 2.84 3.21 3.70 4.15 4.6207 12-hr 0.97 1.13 1.24 1.43 1.63 1.77 2.17 2.63 2.98 3.43 3.84 4.2807 6-hr 0.84 0.97 1.06 1.23 1.40 1.52 1.88 2.26 2.57 2.95 3.31 3.6907 3-hr 0.71 0.83 0.91 1.05 1.20 1.30 1.60 1.93 2.19 2.52 2.82 3.1507 2-hr 0.65 0.76 0.83 0.96 1.09 1.18 1.45 1.75 1.98 2.29 2.56 2.8507 1-hr 0.52 0.61 0.66 0.77 0.87 0.95 1.17 1.42 1.61 1.85 2.07 2.3107 30-min 0.41 0.48 0.52 0.61 0.69 0.75 0.93 1.12 1.27 1.46 1.63 1.8207 15-min 0.30 0.35 0.38 0.45 0.51 0.55 0.68 0.82 0.92 1.06 1.19 1.3307 10-min 0.24 0.28 0.30 0.35 0.40 0.43 0.52 0.63 0.72 0.83 0.93 1.0307 5-min 0.13 0.15 0.17 0.19 0.22 0.24 0.30 0.36 0.41 0.47 0.53 0.59
08 10-day 1.96 2.35 2.71 3.19 3.67 3.99 4.97 6.15 7.02 8.09 8.89 9.7108 5-day 1.59 1.90 2.15 2.49 2.86 3.11 3.92 4.94 5.66 6.58 7.32 8.0508 72-hr 1.45 1.70 1.92 2.22 2.56 2.78 3.43 4.22 4.83 5.70 6.47 7.2908 48-hr 1.35 1.58 1.76 2.04 2.35 2.55 3.15 3.87 4.44 5.26 5.98 6.7708 24-hr 1.28 1.49 1.63 1.89 2.14 2.33 2.86 3.49 3.99 4.70 5.32 6.0408 18-hr 1.20 1.40 1.53 1.77 2.01 2.19 2.69 3.28 3.75 4.42 5.00 5.6808 12-hr 1.12 1.30 1.42 1.64 1.87 2.03 2.49 3.04 3.47 4.09 4.63 5.2508 6-hr 0.96 1.12 1.23 1.42 1.61 1.75 2.14 2.62 2.99 3.52 3.99 4.5308 3-hr 0.82 0.95 1.04 1.21 1.37 1.49 1.83 2.23 2.55 3.01 3.40 3.8708 2-hr 0.74 0.86 0.94 1.09 1.24 1.35 1.66 2.02 2.31 2.73 3.09 3.5008 1-hr 0.61 0.70 0.77 0.89 1.01 1.10 1.34 1.64 1.88 2.21 2.50 2.8408 30-min 0.47 0.55 0.60 0.70 0.79 0.86 1.06 1.29 1.48 1.74 1.97 2.2308 15-min 0.35 0.40 0.44 0.51 0.58 0.63 0.77 0.94 1.08 1.27 1.44 1.6308 10-min 0.27 0.31 0.34 0.40 0.45 0.49 0.60 0.73 0.84 0.99 1.12 1.2708 5-min 0.15 0.18 0.20 0.23 0.26 0.28 0.34 0.42 0.48 0.56 0.64 0.72
09 10-day 1.91 2.30 2.65 3.12 3.59 3.90 4.91 6.09 6.92 7.92 8.62 9.3509 5-day 1.61 1.92 2.17 2.52 2.90 3.15 3.92 4.92 5.66 6.65 7.43 8.2409 72-hr 1.46 1.71 1.94 2.25 2.59 2.81 3.42 4.20 4.82 5.78 6.65 7.5809 48-hr 1.35 1.58 1.76 2.04 2.35 2.55 3.10 3.79 4.39 5.31 6.14 7.0809 24-hr 1.26 1.47 1.60 1.85 2.11 2.29 2.79 3.42 4.01 4.87 5.66 6.5009 18-hr 1.18 1.38 1.50 1.74 1.98 2.15 2.62 3.21 3.77 4.58 5.32 6.1109 12-hr 1.09 1.27 1.39 1.61 1.83 1.99 2.43 2.98 3.49 4.24 4.92 5.6609 6-hr 0.95 1.10 1.20 1.39 1.58 1.72 2.09 2.57 3.01 3.65 4.24 4.8809 3-hr 0.81 0.94 1.03 1.19 1.35 1.47 1.79 2.19 2.57 3.12 3.62 4.1609 2-hr 0.73 0.85 0.93 1.08 1.22 1.33 1.62 1.98 2.33 2.82 3.28 3.7709 1-hr 0.59 0.69 0.76 0.87 0.99 1.08 1.31 1.61 1.88 2.29 2.66 3.0609 30-min 0.47 0.54 0.60 0.69 0.78 0.85 1.03 1.27 1.48 1.80 2.09 2.4009 15-min 0.34 0.40 0.43 0.50 0.57 0.62 0.75 0.92 1.08 1.31 1.53 1.7609 10-min 0.26 0.31 0.34 0.39 0.44 0.48 0.59 0.72 0.84 1.02 1.19 1.3709 5-min 0.15 0.17 0.19 0.22 0.25 0.27 0.33 0.41 0.48 0.58 0.68 0.78
138
Table 8. Concluded
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
10 10-day 1.70 2.04 2.35 2.77 3.18 3.46 4.41 5.58 6.38 7.38 8.09 8.8010 5-day 1.43 1.71 1.93 2.24 2.58 2.80 3.52 4.44 5.07 5.86 6.42 6.9810 72-hr 1.28 1.51 1.70 1.98 2.27 2.47 3.07 3.78 4.32 5.08 5.69 6.3310 48-hr 1.18 1.38 1.54 1.78 2.05 2.23 2.77 3.42 3.94 4.67 5.25 5.8810 24-hr 1.12 1.30 1.42 1.64 1.87 2.03 2.54 3.17 3.64 4.34 4.91 5.5110 18-hr 1.05 1.22 1.34 1.55 1.76 1.91 2.39 2.98 3.42 4.08 4.62 5.1810 12-hr 0.97 1.13 1.24 1.43 1.63 1.77 2.21 2.76 3.17 3.78 4.27 4.7910 6-hr 0.84 0.97 1.06 1.23 1.40 1.52 1.90 2.38 2.73 3.26 3.68 4.1310 3-hr 0.71 0.83 0.91 1.05 1.20 1.30 1.63 2.03 2.33 2.78 3.14 3.5310 2-hr 0.65 0.76 0.83 0.96 1.09 1.18 1.47 1.84 2.11 2.52 2.85 3.2010 1-hr 0.52 0.61 0.66 0.77 0.87 0.95 1.19 1.49 1.71 2.04 2.31 2.5910 30-min 0.41 0.48 0.52 0.61 0.69 0.75 0.94 1.17 1.35 1.61 1.82 2.0410 15-min 0.30 0.35 0.38 0.45 0.51 0.55 0.69 0.86 0.98 1.17 1.33 1.4910 10-mín 0.24 0.28 0.30 0.35 0.40 0.43 0.53 0.67 0.76 0.91 1.03 1.1610 5-min 0.13 0.15 0.17 0.19 0.22 0.24 0.30 0.38 0.44 0.52 0.59 0.66
139
Table 9. Sectional Mean Frequency Distributions for Storm Periods of 5 Minutes to 10 Days
and Recurrence Intervals of 2 Months to 100 Years in Wisconsin
Sectional code (see figure 1 on page 4)
01 – Northwest 06 - East Central02 - North Central 07 - Southwest03 – Northeast 08 - South Central04 - West Central 09 - Southeast05 – Central
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
01 10-day 1.90 2.29 2.64 3.10 3.57 3.88 4.78 5.83 6.58 7.63 8.47 9.3701 5-day 1.55 1.85 2.09 2.42 2.79 3.03 3.75 4.66 5.35 6.27 7.05 7.9001 72-hr 1.39 1.63 1.85 2.14 2.47 2.68 3.31 4.12 4.78 5.67 6.39 7.1601 48-hr 1.30 1.53 1.70 1.97 2.26 2.46 3.05 3.82 4.41 5.23 5.88 6.5601 24-hr 1.22 1.42 1.55 1.80 2.04 2.22 2.77 3.50 4.04 4.79 5.36 5.9801 18-hr 1.15 1.34 1.46 1.69 1.92 2.09 2.60 3.29 3.80 4.50 5.04 5.6201 12-hr 1.06 1.24 1.35 1.56 1.78 1.93 2.41 3.05 3.51 4.17 4.66 5.2001 6-hr 0.91 1.06 1.16 1.34 1.53 1.66 2.08 2.62 3.03 3.59 4.02 4.4901 3-hr 0.78 0.91 0.99 1.15 1.31 1.42 1.77 2.24 2.59 3.07 3.43 3.8301 2-hr 0.71 0.83 0.90 1.04 1.19 1.29 1.61 2.03 2.34 2.78 3.11 3.4701 1-hr 0.57 0.67 0.73 0.84 0.96 1.04 1.30 1.64 1.90 2.25 2.52 2.8101 30-min 0.45 0.52 0.57 0.66 0.75 0.82 1.02 1.30 1.49 1.77 1.98 2.2101 15-min 0.33 0.38 0.42 0.49 0.55 0.60 0.75 0.95 1.09 1.29 1.45 1.6101 10-min 0.26 0.30 0.33 0.38 0.43 0.47 0.58 0.73 0.85 1.01 1.13 1.2601 5-min 0.15 0.17 0.19 0.22 0.25 0.27 0.33 0.42 0.48 0.57 0.64 0.72
02 10-day 1.98 2.39 2.75 3.24 3.73 4.05 4.79 5.68 6.44 7.55 8.49 9.5202 5-day 1.59 1.90 2.15 2.50 2.87 3.12 3.77 4.63 5.33 6.36 7.27 8.2802 72-hr 1.40 1.65 1.86 2.16 2.48 2.70 3.30 4.08 4.72 5.69 6.50 7.4102 48-hr 1.29 1.51 1.68 1.94 2.24 2.43 2.99 3.73 4.31 5.16 5.89 6.6702 24-hr 1.22 1.41 1.55 1.79 2.03 2.21 2.74 3.39 3.90 4.66 5.29 6.0102 18-hr 1.14 1.33 1.46 1.68 1.91 2.08 2.58 3.19 3.67 4.38 4.97 5.6502 12-hr 1.06 1.23 1.34 1.56 1.77 1.92 2.38 2.95 3.39 4.05 4.60 5.2302 6-hr 0.91 1.06 1.16 1.34 1.53 1.66 2.06 2.54 2.93 3.49 3.97 4.5102 3-hr 0.78 0.90 0.99 1.14 1.30 1.41 1.75 2.17 2.50 2.98 3.39 3.8502 2-hr 0.70 0.82 0.90 1.04 1.18 1.28 1.59 1.97 2.26 2.70 3.07 3.4902 1-hr 0.57 0.67 0.73 0.84 0.96 1.04 1.29 1.59 1.83 2.19 2.49 2.8202 30-min 0.45 0.52 0.57 0.66 0.75 0.82 1.01 1.25 1.44 1.72 1.96 2.2202 15-min 0.33 0.38 0.42 0.49 0.55 0.60 0.74 0.92 1.05 1.26 1.43 1.6202 10-min 0.25 0.29 0.32 0.37 0.42 0.46 0.58 0.71 0.82 0.98 1.11 1.2602 5-min 0.15 0.17 0.19 0.22 0.25 0.27 0.33 0.41 0.47 0.56 0.63 0.72
03 10-day 1.78 2.15 2.48 2.91 3.35 3.64 4.45 5.38 6.06 7.01 7.84 8.7403 5-day 1.38 1.65 1.87 2.17 2.49 2.71 3.33 4.08 4.68 5.64 6.47 7.4503 72-hr 1.21 1.42 1.60 1.86 2.13 2.32 2.87 3.59 4.18 5.07 5.84 6.7403 48-hr 1.12 1.31 1.46 1.70 1.95 2.12 2.61 3.28 3.82 4.66 5.38 6.2203 24-hr 1.04 1.22 1.33 1.54 1.75 1.90 2.34 2.94 3.46 4.24 4.94 5.7703 18-hr 0.98 1.15 1.25 1.45 1.65 1.79 2.20 2.76 3.25 3.99 4.64 5.4203 12-hr 0.91 1.06 1.15 1.34 1.52 1.65 2.04 2.56 3.01 3.69 4.30 5.0203 6-hr 0.78 0.91 0.99 1.15 1.31 1.42 1.75 2.20 2.60 3.18 3.70 4.3303 3-hr 0.67 0.78 0.85 0.99 1.12 1.22 1.50 1.88 2.21 2.71 3.16 3.6903 2-hr 0.61 0.70 0.77 0.89 1.01 1.10 1.36 1.71 2.01 2.46 2.87 3.3503 1-hr 0.49 0.57 0.62 0.72 0.82 0.89 1.10 1.38 1.63 1.99 2.32 2.7103 30-min 0.38 0.45 0.49 0.57 0.64 0.70 0.87 1.09 1.28 1.57 1.83 2.1303 15-min 0.28 0.33 0.36 0.41 0.47 0.51 0.63 0.79 0.93 1.14 1.33 1.5603 10-min 0.22 0.26 0.28 0.32 0.37 0.40 0.49 0.62 0.73 0.89 1.04 1.2103 5-min 0.13 0.15 0.16 0.19 0.21 0.23 0.28 0.35 0.42 0.51 0.59 0.69
140
Table 9. Continued
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
04 10-day 1.83 2.21 2.54 2.99 3.44 3.74 4.78 5.94 6.76 7.95 8.96 9.9204 5-day 1.57 1.87 2.12 2.46 2.82 3.07 3.88 4.88 5.69 6.85 7.80 8.8504 72-hr 1.40 1.64 1.86 2.15 2.47 2.69 3.43 4.36 5.17 6.24 7.07 8.0604 48-hr 1.29 1.51 1.68 1.95 2.24 2.44 3.12 3.98 4.66 5.60 6.43 7.3304 24-hr 1.23 1.43 1.56 1.81 2.05 2.23 2.92 3.72 4.40 5.28 6.02 6.8804 18-hr 1.15 1.34 1.47 1.70 1.93 2.10 2.74 3.50 4.14 4.96 5.66 6.4704 12-hr 1.07 1.24 1.36 1.57 1.78 1.94 2.54 3.24 3.83 4.59 5.24 5.9904 6-hr 0.92 1.07 1.17 1.35 1.54 1.67 2.19 2.79 3.30 3.96 4.51 5.1604 3-hr 0.79 0.92 1.00 1.16 1.32 1.43 1.87 2.38 2.82 3.38 3.85 4.4004 2-hr 0.71 0.83 0.90 1.04 1.19 1.29 1.69 2.16 2.55 3.06 3.49 3.9904 1-hr 0.58 0.67 0.73 0.85 0.97 1.05 1.37 1.75 2.07 2.48 2.83 3.2304 30-min 0.46 0.53 0.58 0.67 0.76 0.83 1.08 1.38 1.63 1.95 2.23 2.5504 15-min 0.33 0.38 0.42 0.49 0.55 0.60 0.79 1.00 1.19 1.43 1.63 1.8604 10-min 0.26 0.30 0.33 0.38 0.43 0.47 0.61 0.78 0.92 1.11 1.26 1.4404 5-min 0.15 0.17 0.19 0.22 0.25 0.27 0.35 0.45 0.53 0.63 0.72 0.83
05 10-day 1.90 2.29 2.64 3.10 3.57 3.88 4.77 5.81 6.53 7.59 8.50 9.5205 5-day 1.54 1.84 2.08 2.41 2.77 3.01 3.69 4.49 5.14 6.09 6.94 7.8705 72-hr 1.35 1.59 1.79 2.08 2.39 2.60 3.18 3.89 4.49 5.36 6.09 6.9005 48-hr 1.25 1.46 1.63 1.89 2.17 2.36 2.90 3.58 4.11 4.87 5.48 6.1705 24-hr 1.18 1.38 1.50 1.74 1.98 2.15 2.65 3.25 3.71 4.38 4.93 5.5205 18-hr 1.11 1.29 1.41 1.64 1.86 2.02 2.49 3.06 3.49 4.12 4.63 5.1905 12-hr 1.03 1.20 1.31 1.51 1.72 1.87 2.31 2.83 3.23 3.81 4.29 4.8005 6-hr 0.89 1.03 1.13 1.30 1.48 1.61 1.99 2.44 2.78 3.29 3.70 4.1405 3-hr 0.76 0.88 0.97 1.12 1.27 1.38 1.70 2.08 2.37 2.80 3.16 3.5305 2-hr 0.69 0.80 0.88 1.01 1.15 1.25 1.54 1.88 2.15 2.54 2.86 3.2005 1-hr 0.56 0.65 0.71 0.82 0.93 1.01 1.25 1.53 1.74 2.06 2.32 2.5905 30-min 0.44 0.51 0.56 0.65 0.74 0.80 0.98 1.20 1.37 1.62 1.82 2.0405 15-min 0.32 0.37 0.41 0.47 0.53 0.58 0.72 0.88 1.00 1.18 1.33 1.4905 10-min 0.25 0.29 0.31 0.36 0.41 0.45 0.56 0.68 0.78 0.92 1.04 1.1605 5-min 0.14 0.17 0.18 0.21 0.24 0.26 0.32 0.39 0.45 0.53 0.59 0.66
06 10-day 1.70 2.05 2.36 2.78 3.19 3.47 4.28 5.29 6.11 7.36 8.51 9.8506 5-day 1.34 1.60 1.81 2.10 2.42 2.63 3.22 4.01 4.74 5.91 6.98 8.2806 72-hr 1.21 1.42 1.61 1.66 2.14 2.33 2.83 3.55 4.20 5.25 6.23 7.4206 48-hr 1.13 1.32 1.47 1.70 1.96 2.13 2.61 3.26 3.87 4.86 5.77 6.8806 24-hr 1.08 1.25 1.37 1.59 1.80 1.96 2.40 3.00 3.56 4.46 5.32 6.3506 18-hr 1.01 1.18 1.29 1.49 1.69 1.84 2.26 2.82 3.35 4.19 5.00 5.9706 12-hr 0.94 1.09 1.20 1.39 1.57 1.71 2.09 2.61 3.10 3.88 4.63 5.5206 6-hr 0.81 0.94 1.03 1.19 1.35 1.47 1.80 2.25 2.67 3.35 3.99 4.7606 3-hr 0.69 0.80 0.88 1.01 1.15 1.25 1.54 1.92 2.28 2.85 3.40 4.0606 2-hr 0.63 0.73 0.80 0.92 1.05 1.14 1.39 1.74 2.06 2.59 3.09 3.6806 1-hr 0.51 0.59 0.64 0.75 0.85 0.92 1.13 1.41 1.67 2.10 2.50 2.9806 30-min 0.40 0.47 0.51 0.59 0.67 0.73 0.89 1.11 1.32 1.65 1.97 2.3506 15-min 0.29 0.34 0.37 0.43 0.49 0.53 0.65 0.81 0.96 1.20 1.44 1.7106 10-min 0.23 0.26 0.29 0.33 0.38 0.41 0.50 0.63 0.75 0.94 1.12 1.3306 5-min 0.13 0.15 0.17 0.19 0.22 0.24 0.29 0.36 0.43 0.54 0.64 0.76
141
Table 9. Concluded
Rainfall (inches) for given recurrence interval
Section Duration 2-month 3-month 4-month 6-month 9-month 1-year 2-year 5-year 10-year 25-year 50-year 100-year
07 10-day 1.85 2.23 2.57 3.02 3.48 3.78 4.88 6.19 7.16 8.45 9.49 10.6007 5-day 1.56 1.87 2.11 2.45 2.82 3.06 3.92 5.04 5.91 7.22 8.29 9.5207 72-hr 1.40 1.65 1.86 2.16 2.48 2.70 3.42 4.43 5.23 6.43 7.49 8.6807 48-hr 1.31 1.53 1.70 1.98 2.27 2.47 3.12 4.05 4.82 5.91 6.88 7.9507 24-hr 1.24 1.44 1.57 1.82 2.07 2.25 2.82 3.60 4.31 5.29 6.17 7.1507 18-hr 1.17 1.36 1.48 1.72 1.95 2.12 2.65 3.38 4.05 4.97 5.80 6.7207 12-hr 1.08 1.25 1.37 1.59 1.80 1.96 2.45 3.13 3.75 4.60 5.37 6.2207 6-hr 0.93 1.08 1.18 1.37 1.55 1.69 2.12 2.70 3.23 3.97 4.63 5.3607 3-hr 0.79 0.92 1.01 1.17 1.32 1.44 1.80 2.30 2.76 3.39 3.95 4.5807 2-hr 0.71 0.83 0.91 1.05 1.20 1.30 1.64 2.09 2.50 3.07 3.58 4.1507 1-hr 0.58 0.68 0.74 0.86 0.98 1.06 1.33 1.69 2.03 2.49 2.90 3.3607 30-min 0.46 0.53 0.58 0.67 0.76 0.83 1.04 1.33 1.59 1.96 2.28 2.6507 15-min 0.34 0.39 0.43 0.49 0.56 0.61 0.76 0.97 1.16 1.43 1.67 1.9307 10-min 0.26 0.30 0.33 0.38 0.43 0.47 0.59 0.76 0.91 1.11 1.30 1.5007 5-min 0.15 0.17 0.19 0.22 0.25 0.27 0.34 0.43 0.52 0.63 0.74 0.86
08 10-day 1.82 2.19 2.52 2.97 3.41 3.71 4.72 5.93 6.86 8.21 9.33 10.6008 5-day 1.52 1.82 2.06 2.39 2.75 2.99 3.78 4.86 5.73 7.03 8.14 9.3608 72-hr 1.40 1.65 1.86 2.16 2.48 2.70 3.38 4.34 5.16 6.34 7.34 8.4708 48-hr 1.30 1.53 1.70 1.97 2.26 2.46 3.07 3.96 4.68 5.79 6.75 7.8208 24-hr 1.24 1.44 1.57 1.82 2.07 2.25 2.78 3.53 4.20 5.18 6.06 7.0608 18-hr 1.17 1.36 1.48 1.72 1.95 2.12 2.61 3.32 3.95 4.87 5.70 6.6408 12-hr 1.08 1.25 1.37 1.59 1.80 1.96 2.42 3.07 3.65 4.51 5.27 6.1408 6-hr 0.93 1.08 1.18 1.37 1.55 1.69 2.09 2.65 3.15 3.88 4.55 5.3008 3-hr 0.79 0.92 1.01 1.17 1.32 1.44 1.78 2.26 2.69 3.32 3.88 4.5208 2-hr 0.71 0.83 0.91 1.05 1.20 1.30 1.61 2.05 2.44 3.00 3.51 4.0908 1-hr 0.58 0.68 0.74 0.86 0.98 1.06 1.31 1.66 1.97 2.43 2.85 3.3208 30-min 0.46 0.53 0.58 0.67 0.76 0.83 1.03 1.31 1.55 1.92 2.24 2.6108 15-min 0.34 0.39 0.43 0.49 0.56 0.61 0.75 0.95 1.13 1.40 1.64 1.9108 10-min 0.26 0.30 0.33 0.38 0.43 0.47 0.58 0.74 0.88 1.09 1.27 1.4808 5-min 0.15 0.17 0.19 0.22 0.25 0.27 0.33 0.42 0.50 0.62 0.73 0.85
09 10-day 1.81 2.18 2.52 2.96 3.40 3.70 4.55 5.65 6.58 7.89 9.09 10.4909 5-day 1.50 1.79 2.03 2.35 2.70 2.94 3.66 4.66 5.50 6.72 7.85 9.1409 72-hr 1.36 1.60 1.81 2.10 2.41 2.62 3.25 4.14 4.85 5.90 6.84 7.8009 48-hr 1.27 1.49 1.66 1.92 2.21 2.40 2.98 3.78 4.43 5.36 6.22 7.1409 24-hr 1.20 1.40 1.53 1.77 2.01 2.18 2.70 3.33 3.86 4.66 5.38 6.2409 18-hr 1.13 1.31 1.43 1.66 1.89 2.05 2.54 3.13 3.63 4.38 5.06 5.8709 12-hr 1.04 1.22 1.33 1.54 1.75 1.90 2.35 2.90 3.36 4.05 4.68 5.4309 6-hr 0.90 1.04 1.14 1.32 1.50 1.63 2.03 2.50 2.89 3.49 4.03 4.6809 3-hr 0.77 0.90 0.98 1.13 1.29 1.40 1.73 2.13 2.47 2.98 3.44 3.9909 2-hr 0.69 0.81 0.88 1.02 1.16 1.26 1.57 1.93 2.24 2.70 3.12 3.6209 1-hr 0.56 0.65 0.71 0.83 0.94 1.02 1.27 1.57 1.81 2.19 2.53 2.9309 30-min 0.45 0.52 0.57 0.66 0.75 0.81 1.00 1.23 1.43 1.72 1.99 2.3109 15-min 0.32 0.38 0.41 0.48 0.54 0.59 0.73 0.90 1.04 1.26 1.45 1.6809 10-min 0.25 0.29 0.32 0.37 0.42 0.46 0.57 0.70 0.81 0.98 1.13 1.3109 5-min 0.14 0.17 0.18 0.21 0.24 0.26 0.32 0.40 0.46 0.56 0.65 0.75